IPDS 2006 Integrated Powertrain and Driveline Systems 2006

IPDS 2006 Integrated Powertrain & Driveline Systems 2006 Conference Organising Committee Nick Vaughan (Chairman) Geof...

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IPDS 2006 Integrated Powertrain & Driveline Systems 2006

Conference Organising Committee Nick Vaughan (Chairman) Geoff Davis Chris Brace Adrian Cole Derek Eade Dave Simner Anthony Thompson

Cranfield University Ricardo UK Ltd University of Bath TIC University of Hertfordshire Cranfield University Lotus Cars

IPDS 2006 Integ rated Powertrai n & Driveline Systems 2006 14-15 June 2006 Ford Motor Company, Dunton, Essex Institution of Mechanical Engineers Automobile Division

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CRC Press Boca Raton Boston New York Washington, DC

WOODHEAD PUBLISHING LIMITED Cambridge England

Published by Woodhead Publishing Limited, Abington Hall, Abington, Cambridge CBI 6AH, England www.woodheadpublishing.com Published in North America by CRC Press LLC, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487, USA First published 2006, Woodhead Publishing Limited and CRC Press LLC © 2006, Institution of Mechanical Engineers unless otherwise stated The authors have asserted their moral rights. This book and CD-ROM contain information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book or CDROM. Neither this book, CD-ROM, nor any part thereof may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from Woodhead Publishing Limited. The consent of Woodhead Publishing Limited does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing Limited for such copying. Further terms and conditions concerning the CD-ROM are included on the CD-ROM. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Library of Congress Cataloging in Publication Data A catalog record for this book is available from the Library of Congress. Woodhead Publishing ISBN-13: 978-1-84569-197-4 Woodhead Publishing ISBN-I 0: 1-84569-197-0 CRC Press ISBN-IO: 0-8493-8888-0 CRC Press order number: WP8888 Printed by Antony Rowe Limited, Chippenham, Wilts, England

CONTENTS Section 1: Transmissions The investigation of automotive transmission error characteristics subject to operating conditions G Davis, Ricardo UK Ltd, UK and P Brooks, University of Leeds, UK

3

Comparison of approximation methods applied to a complex nonlinear analytical transmission model R H Cornish and Y H Siew, Technology Innovation Centre, UK, and J A Pears, Romax Technology, UK

11

Gear teeth impacts in hydrodynamic conjunctions: idle rattle o Tangasawi, S Theodossiades and H Rahnejat, Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, UK, and P Kelly, Ford Werke AG, Germany

19

The prediction of loaded static tooth behaviour for automotive parallel axis gears A Leavitt and P Brooks, University of Leeds, UK, and D Parkin-Moore, Ricardo UK Ltd, UK

29

Boundary lubrication film formation from belt type CVT fluids K Narita, fdemitsu Kosan Company Ltd, Japan, and M Priest, Institute of Tribology, University of Leeds, UK

39

Design consideration and potential of the Milner CVT A Hunt and S Akehurst, University of Bath, UK, and S Schaaf, fntersyn Technologies, Texas, USA

51

Section 2: Concept to Market Evolution Optimum engine models for diesel automotive powertrain development processes R P Osborne and N Weaver, Ford Motor Company Ltd, UK

67

Hardware-in-the-Ioop simulation (HIL) for production engine development C Haukap, K Ropke and B Barzantny, fA V GmbH - Ingenieurgesellschaft Auto und Verkehr, Germany

77

Section 3: Powertrain Integration An integrated simulation approach: Ricardo Transmission and Driveline Dynamic Simulation Library F V Brandao and P A Harman, Ricardo UK Ltd, UK

v

87

A powertrain thermal simulation model J Hartland and A Robertson, Jaguar Cars Ltd, UK

95

Novel techniques for holistic powertrain optimisation - a hybrid vehicle case study C M Crewe, J Seabrook and F V Brandtlo, Ricardo UK Ltd, UK, and S P Edwards, Ricardo Deutschland GmbH, Germany

105

Hybridization of a small SUV and its control B Cho and N D Vaughan, Cranfield University, UK

117

Concept and potential of 'CVT-hybrid-driveline' B-R Hahn, H Pflaum and D Tomic, Gear Research Centre, Technical University Munich, Germany

127

Power combining single regime transmissions for automotive vehicles F Moeller, NexxtDrive Ltd, UK

141

Section 4: Engine Integration Driveability validation using MAHLE Powertrain's IDAA toolset D Baker, T Girling, G Kennedy, D Pates, and B Porter, MAHLE Powertrain Ltd, UK

153

The effect of exhaust aftertreatment and engine temperature on IOLs for CVT powertrains J A Gutierrez Magana and C J Brace, University of Bath, UK

169

A turbocharged diesel engine with intake air accumulator to improve torque transients in a CVT powertrain Y Rohrbacher, B Bonnet and N D Vaughan, Cranfield University

189

vi

THE INVESTIGATION OF AUTOMOTIVE TRANSMISSION ERROR CHARACTERISTICS SUBJECT TO OPERATING CONDITIONS Dr. Geoffrey Davis Ricardo UK Ltd. Midlands Technical Centre. Leamington Spa. CV32 1FQ. Email: [email protected] Dr. Peter Brooks School of Mechanical Engineering. University Of Leeds. Leeds. LS2 9JT Email: [email protected]

ABSTRACT In the current automotive market, the increasing demand for improved vehicle refinement has led to vehicle manufacturers dedicating ever increasing budgets to the reduction of airborne and structure borne noise. Geared components of the drivetrain, specifically transmissions and final drives, can make a significant contribution to the combined noise and vibration that manifests itself externally or within the passenger compartment. It is the desire of all drivetrain engineers to reduce these sources of noise, thereby improving vehicle refinement, whilst maintaining the geared components required functionality. Despite decades of research there is still a need for improved understanding of the role that gears and the particulars of their design play in producing system excitation and therefore noise. This paper details the investigation of automotive transmission gear pair behaviour. The specific parameter of interest is transmission error, which is widely recognised as a major source of high frequency excitation in automotive transmissions, and fundamental to mesh frequency components of audible noise. Following an extensive literature review (1, 2) the authors identified the need for further experimental investigation of the behaviour of Transmission Error when subject to typical automotive operating conditions, variations in macro and micro geometry and gears of typical automotive quality. To aid investigation it was necessary to develop a suitable experimental facility. Following the development of an advanced gear pair test rig a series of extensive tests were conducted, the findings of which highlighted a series of clear trends that exist between operating conditions, changes in geometry and Transmission Error.

NOTATION Transmission Error, radians Angular rotation of the input and output gears, radians Number of teeth on the input and output gears Fp fp fi' Fi' Fp'

Cumulative pitch error, microns Adjacent pitch error, microns l/tooth Transmission Error component, microns Total composite Transmission Error, microns Cumulative working pitch error, microns

3

INTRODUCTION

The continued requirement for improved vehicle refinement has forced Ricardo's engineers to dedicate significant resource to reducing the drivetrains NVH contribution. During a specific piece of consultancy work (3) Ricardo engineers attempted to predict audible gear whine for a spur and helical geared transmission. Despite reasonable correlation the work failed to accurately predict absolute noise levels. The likeliest source of error within the analysis was deemed the accurate prediction of gear mesh based behaviour; specifically tooth contact behaviour and the resultant torsional excitement caused as a result. At the time Ricardo had no specific research programme dedicated to investigating the fundamental sources of gear mesh excitation and in particular a physical parameter known as Transmission Error (TE). A subsequent research programme in conjunction with the School of Mechanical Engineering at the University of Leeds proceeded with the goal of creating a suitable experimental test facility suitable for the measurement of TE in automotive gear pairs subject to operating conditions. An outline of the findings of this research are included in the following paper, and full details can be found in the additional work of the author. To date a significant quantity of gear noise and vibration related research has been conducted within the United Kingdom and worldwide. The early work (4) conducted at The University of Cambridge is recognised as the cornerstone of much of the research conducted since. The relevance of much of the previous work to automotive transmission applications is questionable. Despite numerous researchers modelling and experimentally investigating the effect of various geometrical parameters on TE, gear dynamic behaviour and noise, much of the correlation and investigation has occurred at inappropriate operating conditions and has been based on gears not typical of those found in the automotive environment. Thus the investigation of gear behaviour subject to typical automotive operating conditions (variable speed, variable torque, mesh misalignment) and with gears of automotive quality and dimension has seen an inadequate amount of dedicated attention since research into dynamic gear pair behaviour started some fifty years ago. AUTOMOTIVE TRANSMISSIONS, GEARS & TRANSMISSION WHINE

Transmission casings are excited by a combination of internal acoustic pressure, internal steady state wall pressure and gear mesh orientated excitation transmitted via the bearings. Numerous authors (5,6,7,8) have shown that Transmission Error (TE) is recognised as the most prominent source of gear mesh based excitation, with the time varying component periodic at mesh frequency directly related to noise. The standard definition for TE is as follows - Transmission Error is the deviation in the position of the driven gear (for any given position of the driving gear), relative to the position that the driven gear would occupy if both gears were geometrically perfect and undeformed - and can be calculated as follows: (1)

TE is caused by the non-conjugate motion of meshing gear pairs, causing high frequency vibrations that excite the gears themselves and in tum the shafts and bearings, Figure 1. Although gear body vibration generates airborne noise, the transmission

4

housing acts as a poor transmitter of sound so little of the original airborne noise reaches the external environment. The noise is predominantly structure borne and the dynamic forces generated are transmitted via the bearings which excite the housing, with the level of exciting force largely dependent on the magnitude of TE, the dynamic response of the gears and the supporting structure. The surfaces of the vibrating transmission housing act like a loudspeaker and propagate the audible noise that is heard. The vibration is also transmitted via the gearbox mounts to the chassis and enters the cabin through alternative paths.

Mesh Excitation

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Housing Vibration

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Figure 1. Gear noise transmission path (6). The fundamentals of TE are discussed in numerous publications (2). In essence, if the gear teeth of a meshing gear pair of identical geometry were of a perfect involute profile, rigid and correctly spaced around the base circle radius then there would be no rotational error. In practice this does not occur and it is as a result of TE. When a gear pair mesh freely under no torque and at a minimum speed, the measured TE is due to the geometrical variations from the perfect involute profile of the meshing tooth geometry. When subject to load and speed, tooth stiffuess and inertia effects are present and true representation of these effects shows clear variation with the no load TE signal. Therefore TE is considered to exist in one of three forms; static, quasi-static and dynamic. Subject to minimum torque and speed, static TE is attributed to geometrical and manufacturing deviations only. However, as the gear geometry becomes more complex additional design parameters such as helix angle actively contribute to the measured TE. Quasi-static TE is measured under sufficient load to induce mesh deformation, yet at a sufficiently low speed to render dynamic effects negligible, and with the addition of torque the contribution of mesh-stiffuess variation, gear body distortion and mesh misalignment to the TE signal can be observed. Dynamic TE is subject to operating speeds and torques and therefore considers inertia, mesh stiffness and the dynamic response of the meshing gear pair. A typical time domain static TE trace is shown in Figure 2. The TE signal consists of two fundamental components, the once per rev (llrev) and once per tooth (l/tooth) components. The lIrev component is the low frequency large amplitude signal whilst the lItooth component is the high frequency small amplitude signal that occurs at TMF. Analysis of the composite TE signal in the frequency domain enables determination of the magnitude of the IIrev and IItooth frequency components and their relative

5

hannonics. Where transmission noise, otherwise known as whine is the primary interest then the lItooth component ofTE and its hannonics are significant. Composite Transmission ElTor with l/rev component superimposed I

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Figure 2 Static composite TE trace with lIrev component filtered and superimposed. There are many operational and geometrical parameters that influence TE and therefore noise (2). In brief these include; • Operational and system parameters Mesh misalignment, gear body defonnation, lubrication regime, bearings selection and design, transmission housing optimisation. • Geometrical parameters Manufacturing errors such as pitch, profile and pressure angle errors. Optimisation of key macro and micro-geometrical design parameters, Previous research has investigated the implications of many of these parameters on TE for non-automotive applications and has also focused on the reduction of TE through optimisation of tooth geometry for set operating conditions. Despite the benefits of macro and micro geometrical optimisation being clear, the application to gears that are subject to a wide range of operating conditions and varying levels of quality requires further investigation to improve understanding.

EXPERIMENTAL TEST FACILITY DEVELOPMENT In order to meet the requirements of a dedicated research tool (1) developed exclusively for the investigation of TE in automotive transmission applications, the experimental test rig would be required to measure TE of typical automotive gears subject to a range of operating conditions. The authors developed a dedicated test rig using an existing 5 speed front wheel drive transverse transmission as a basis. The gearbox in question

6

utilised an overhung sth speed gear pair, which rendered it particularly suitable for experimental investigation of TE with optical encoders. Therefore the test rig was developed into a suitable test bed for the measurement of TE and gear pair mesh misalignment of the overhung sth speed gear pair only. The transmission casing was removed and replaced with a dedicated set of fixtures, which retained key design attributes of the original transmission such as centre distances. Only key internals (primary and main shafts, final drive and differential) were retained. All gears (except sth), synchronisers and shift system components were removed. At the input the clutch mechanism and all ancillaries were removed and replaced with a dedicated coupling and flange arrangement. Drive was transferred from the differential through a modified splined driveshaft which mated to the internal spline of the differential. A series of dedicated sth gear pair test specimens were designed and manufactured to replace the original gears and therefore enable component testing and experimental investigation. Tufnol shaft and torsionaly stiffCY coupling assembly

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I Removable mounting unit and encoder housing assembly

Figure 3. Optical incremental shaft encoders and accompanying shaft assembly. In order to measure TE two SV TTL 36000 line optical incremental shaft encoders were used. These were attached to the overhung Sth speed gear pair by means of a torsionally stiff shaft, bearing and coupling arrangement which isolated the optical encoders from the Sth speed gear pair mesh misalignment, Figure 3. In order to measure mesh misalignment, a series of test gear specimens were fitted with target discs. Once fitted to the gear body the measurement surface of the disc was machined true so that it was perpendicular to the longitudinal axis of the shaft. A series on non-contacting eddy current displacement transducers were located at 90° intervals around each target disc thus enabling accurate measurement of the deviation of the surface of the target disc and therefore mesh misalignment. A key requirement for the accompanying data acquisition system was the synchronous and simultaneous measurement of the rotational position of each incremental encoder and therefore gear. This system attribute was vital for accurate measurement of TE. In order to achieve this requirement a combination of dedicated counter/timer hardware (for accurate TE measurement) in conjunction with high speed AID hardware (mesh misalignment measurement and all other ancillary signals) and a dedicated PC were used (2). This enabled accurate measurement of TE and misalignment simultaneously with a TE signal resolution of ~O.l arc seconds.

7

EXPERIMENTAL INVESTIGATION During the experimental research several areas of investigation were conducted using a series of dedicated automotive gear specimens. In summary these investigations focused on the implications of gear design geometry on measured TE and the comparison between TE measured subject to static, quasi-static and dynamic conditions. The following section gives an overview of some of the key findings. Using the captured data the following plots (Figure 4 through to Figure 6) illustrate a selection of the results. Figure 4 shows the benefit of increased total contact ratio on TE for a gear pair subject to a range of applied torque under quasi static conditions. The results clearly show that for the basic gear design the amount and extent of tip relief was optimised for an applied torque of ~30Nm resulting in minimum TE at this torque, however for higher levels of torque the mesh deformation increases significantly resulting in increased TE. An increase in total contact ratio counters the increase in TE at higher torques, with the gear pair with the highest total contact resulting in the lowest amount of TE across the operating range. These results clearly indicate the benefit in maximising total contact ratio for reducing TE and therefore reducing noise. 3.0

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Figure 4. Variation in the median value of the peak-to-peak lIper tooth component of TE, for gears subject to quasi-static operating conditions (lS0rpm) with a variation in Total Contact Ratio. Figure 5 presents test data for the same gear specimens when subject to an identical range of applied torque, but dynamic operating conditions (27S0rpm versus lS0rpm). These results show very different behaviour in terms or reduced TE for increased Total Contact Ratio. In this case the gear with the greatest value of Total Contact Ratio does not result in the least TE across the operating window. These results highlight the importance of considering system dynamics when optimising gear designs for low TE and low noise. With specific focus on contact ratio further benefits can be made by optimising the Transverse Contact Ratio and Helical Overlap Ratio independently. Use

8

of integer values and a maximum value of Total Contact Ratio will yield significant benefits in producing a low value ofTE and therefore low noise (2). 3.0

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Figure 6. Comparison of the variation in the median value of the peak-to-peak lIper tooth component of TE, for gears subject to quasi-static and dynamic operating conditions and identical values of Total Contact Ratio

9

The difference between results obtained from quasi-static and dynamic conditions is further highlighted in Figure 6 which compares quasi-static and dynamic data for two gear pairs with a similar value for Total Contact Ratio. The quasi-static data indicate a marginal improvement by increasing Total Contact Ratio. When subject to dynamic conditions, not only are the overall values of TE lower when the gear pair are subject to dynamic conditions but the benefit from a marginal increase in Total Contact Ratio is significantly greater than that shown for quasi-static operating conditions. Once again this highlights the risks associated from using knowledge gained solely through quasistatic based test data when developing low noise transmissions.

CONCLUSIONS The test results have shown that the dedicated gear pair test rig and data acquisition system has enabled accurate measurement ofTE for automotive gears subject to a range of operational conditions. From the test data shown it is clear that TE behaviour when subject to dynamic conditions differs greatly from that tested under quasi-static conditions, highlighting misleading conclusions that maybe made from quasi-static test data and any analytical approach that does not consider system dynamics. Furthermore, the results clearly show that simply increasing the Total Contact Ratio does not guarantee a low noise automotive transmission. Careful thought must be given to fine tuning the individual values of Involute and Transverse Contact Ratio to guarantee a low value ofTE and therefore noise.

REFERENCES 1. Davis, G. Brooks, P. Findlay, M. (2001), "Recent Advances in Automotive Gear Pair Dynamic Behaviour Measurement and Prediction - A Review", MPT2001 JSME International Conference on Motion and Power Transmissions, November 15-17, 2001, Fukuoka, Japan, pp.90-96, Japanese Society of Mechanical Engineers. 2. Davis, G (2004), "An Investigation of Automotive Transmission Error Characteristics, Subject To Operational Conditions", PhD Thesis, University of Leeds, England. 3. Beacham, M. R. Bell, D. J. Powell, N. N. Savage, M. T. (1999), "Development of Transmission Whine Prediction Tools", SAE Technical Paper Series, SAE 99NV - 101. 4. Harris, S. L. (1958), "Dynamic Loads on the Teeth of Spur Gears", Proceedings of the IMechE, Vol.172, No.2, pp.87-112. 5. Welbourn, D. B. (1979), "Fundamental Knowledge of Gear Noise - A Survey", Proceedings of the IMechE, Cll17179, pp.9-14, 1979, Institute of Mechanical Engineers, London. 6. Townsend, D. P. (1991), "Dudley's Gear Handbook", Second Edition, McGraw-Hill Inc, ISBN 0-07-017903-4 7. Munro, R. (1991), "An Analysis of Some of Niemann's Gear Noise Measurements of Spur Gears", MPT'91 JSME International Conference on Motion and Powertransmissions, November 23-26,1991, Hiroshima, Japan, JSME, pp.lO-14. 8. Smith, R. E. (1988), "The Relationship of Measured Gear Noise to Measured Gear Transmission Errors", Gear Technology Magazine, Jan-Feb 1988, pp.38-47. © Ricardo PIc. 2006

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Comparison of Approximation Methods Applied to a Complex NonLinear Analytical Transmission Model R. H. Cornish!, Y. H. Siew!, J. A Pears 2 I Technology Innovation Centre, Millennium Point, Curzon Street, Digbeth, Birmingham B47XG, United Kingdom 2 Romax Technology, UK Head Office, Rutherford House, Nottingham Science and Technology Park, Nottingham, Nottinghamshire, NG7 2PZ, United Kingdom

ABSTRACT This paper describes an investigation into a number of approximation methods applied to a non-linear analytical model of a truck planetary hub reduction unit. The effective transmission error of the planetary set is calculated using the proprietary transmission software, RomaxDesigner, for a range of design variants to generate a training set of data. Three approximation models are then derived to fit the training data, and the accuracy of each model assessed. The potential use of the approximation models with regard to optimisation and design sensitivity studies is discussed and the expected time savings described. INTRODUCTION Computer based simulation codes are widely used in the automotive industry for a large number of sophisticated calculations, including computational fluid dynamic analysis, finite element analysis and domain specific software packages. However, as the problems become more complex, the run-times for these analyses can be of the order of hours, or even days. It is still the case that processor speeds are exceeded by the engineering drivers. Even short run-times limit the usefulness of the simulation codes in design optimisation and tolerance sensitivity studies where many repeat runs are necessary.

Approximation methods are a useful tool in which the computationally expensive simulation code is replaced with an approximate, non-physical numerical model, which can be evaluated in a short time. This "approximation model" (sometimes called a surrogate model or metamodel) can then be used for optimisation, design space exploration and tolerance sensitivity studies etc. This paper describes an initial investigation into the application of approximation methodologies to a complex non-linear problem using the proprietary software package, RomaxDesigner. RomaxDesigner is the "best-in-class" software package for integrated gearbox and driveline engineering and is used worldwide by the majority of automotive OEMs to accelerate their design process and to simulate durability and refinement.

11

Fig. 1: A typical 4 planet epicyclic gearset

The specific engineering problem chosen for this study was the investigation of a planetary gear set in the hub reduction unit of a truck (Fig. 1). The effective transmission error of the planetary gearset is known to have a significant impact on both the gear whine noise and the dynamic behaviour of the whole driveline. It is strongly influenced by the many design parameters of the planetary gearset, and can be uniquely predicted by RomaxDesigner with a run time of approximately 5 minutes on a typical desktop PC. This run time may be reasonably short, but is sufficient to illustrate the possible advantages of using approximation methods. This academic project has been supported by Romax Technology, UK. Approximation Methods For this project, three approximation methods are used for comparison, namely Polynomial Cubic Expansion, Neural Network and Radial Basis Function. Polynomial Cubic Expansion The Polynomial model is based on ideas from the Taylor series. It is the most commonly used model in experiment design because it is easy to apply, based as it is on the relation of several input factors and one response output. Generally the polynomial has the form as shown below. y=

b o + b1Xl+ b2X2+ b3X3+ b12XIX2+ b13xlX3 + b23X2X3+ b123XIX2XJ+ b 11 X1 2+ b 22 x/+ b33X32 + e

where Y is the output response, b are the model coefficients and x is the input variable. The coefficients are chosen to minimize the error e, with the training data. The polynomial works well where the data is smooth and has no sudden changes. Neural network The idea for an Artificial Neural Network is based on the nervous system of a human brain which consists of 100 billion of cells known as neurons (Fig. 1) that are connected with each other. Generally, each neuron will be connected to about ten of thousand others. A neuron can receive multiple inputs while sending one output. The mathematical formula for one perceptron is shown below. The inputs are multiplied by weights (w) which represent the synaptic strength.

12

where Y is the response of the neuron, Wi is the weight of the ith input value, Xi is the ith input value, b is the threshold for the neuron and f is the transfer function or activation function. Radial Basis Function A radial basis function model is represented by the equation below:

i

where Y is the output response, Wi is the weight of the ith neuron, is the univariate function, X is the input n-dimensional vector, ~ is an n dimensional vector locating the radial basis centre. The modulus term represents the Euclidean distance of the input vector from the radial basis centre. The assumption is that the nearer the input vector is to the centre point of the neuron the stronger the reaction. The radial basis function is said to be advantageous compared with a multilayer neural network in having less tendency towards oscillatory behaviour. Engineering Application - Planetary Transmission Error Planetary Gears

Planetary gearsets are widely used in automatic transmission systems of cars as well as in truck wheel hubs to increase torque to the wheel, whilst keeping the torque low in the rest of the driveline. A planetary hub reduction unit is typically used in mining, logging, and heavy hauling trucks which have high gross weight requirement. This set of planetary gears is situated within the wheel hub that connects the wheel hub to the axle shaft. The sun is connected to the drive axle shaft and the ring gear is fixed to the axle housing while the planetary carrier is connected to the wheel hub as a power output. RomaxDesigner In modeling the planetary gear, the specialist transmission software RomaxDesigner has been used. It allows the design and analysis of the driveline in one package and contains a fully coupled non-linear algorithm to analyse the shaft/bearinglgearlhousing static system.

The gears, planetary gear system, bearings and shafts are modelled as analysis objects with correlation to validated tests. This makes it much easier and faster to enter these components by just keying in the design parameters and editing their attributes. RomaxDesigner can calculate all the gear meshing points, forces, load distribution and take into account all the boundary connections. The housing finite element models of planetary carriers and housings are condensed in RomaxDesigner to a reduced stiffness matrix and coupled with the internal transmission through the bearing nodes. The stiffness sub-matrix for a rolling element bearing, linking the displacements and tilts of the inner and outer raceway geometric centres, is obtained as the slope of the force versus deflection curve at the bearing's operating displacements. The stiffness terms are obtained from detailed bearing models which include the contacts of the

13

rolling elements with the raceways. The non-linear effects of internal clearance, preload and centrifugal effects in high-speed bearings are effectively modelled. The transmission system model built by this approach is very compact compared to conventional finite element models. The modelling time is significantly reduced and many possible modelling errors, which may inevitably happen in a conventional finite element approach, can be avoided. Planetary Transmission Error Prediction The model is first built in the RomaxDesigner software - for a planetary hub reduction unit such as this, this typically takes less than one day (Fig. 2). The model contains all of the information needed for the analysis, including bearing details, clearances, shaft dimensions, flexibility of housings/ring gears/planet carriers, and tooth micro-geometry details.

Fig. 2: A planetary hub reduction unit built on Romax Designer software A full system quasi-static analysis is then performed at each rotational position of the planetary unit. This analysis includes the effects of: •

Time-varying misalignment due to shaft, bearing and housing deflections.

•

Load (torque) sharing between planets. A calculation of how the torque is shared between the planets is important, this will also change with time and is dependent on many factors including the backlash of the individual gear mesh (perhaps due to manufacturing errors), and the stiffness of the gear mesh.

•

Relative phasing of planetary gear meshes, including the phasing between the various sun-planet meshes, phasing between the various ring-planet meshes, and phasing between the ring-planet and sun-planet meshes of a given planet.

It is important to note that the analyses are solved simultaneously. For example, it is not possible to solve the shaft-bearing system to predict the mesh misalignment, then use this misalignment to predict the transmission error at the gear mesh. This is because the details of the tooth contact are not only influenced by the misalignment, but the misalignment is influenced by the tooth contact.

14

The effective transmission error is then the relative rotational position of the input to the system (in this case, the sun shaft) and the output of the system (the planet carrier), as the system rotates. The input parameters of interest in the model were: Gear micro geometry: •

Sun gear lead crown

•

Sun gear lead slope

•

Planet gear lead crown

•

Planet gear lead slope

Planet spacing: •

Rotational position of planet gear one and three

Bearing Clearances: •

Radial internal clearance of needle roller bearings under planet gears

The output result of interest was the first harmonic of the effective transmission error trace, that is, the magnitude of the rotational displacement difference at the tooth meshing frequency. This analysis typically takes approximately five minutes. Calculation of Approximation Models To produce the approximation models, the "exact solution" (i.e. the RomaxDesigner analysis) has to be evaluated for a range of input parameters to produce a "training set" of data. The sampling of the design space to produce the training set of data can be performed by a number of methods. In this case, 115 design points were manually selected. The range of modifications covered is shown below in Table 1.

The design data points were defined in Microsoft Excel and then imported into RomaxDesigner for automatic batch running analysis. To evaluate the 125 design point training set took approximately 10 hours, and the results exported as a text file. The training set data was then imported into Matlab's Model Base Calibration toolbox to calculate the three approximation models. Each of these were each calculated in less than 3 minutes. The polynomial model was 3rd order with 2 levels of interaction, the radial basis function model was multi-quadratic and the neural network model had 2 hidden layers, with 10 neurons in layer 1 and 5 in layer 2. To evaluate the accuracy of the approximation models, a validation set of 20 design points (all different to the training set) was evaluated in RomaxDesigner and also in the three approximation models. Each evaluation in RomaxDesigner took approximately five minutes, each evaluation using an approximation method took less than 1 second. RESULTS The calculated results for the validation points are shown below for the "exact solution" calculated in RomaxDesigner and for each of the approximation methods. The mean and standard deviation of the absolute values of the errors for each of the approximation methods are shown in Table 3 below.

15

DISCUSSION From the results obtained from the analysis, it can be seen that the prediction levels portray an excellent level of prediction accuracy of less than 1% of error for the neural network model. This is significantly better than the accuracy of both the polynomial cubic expansion and the radial basis function models. The total time to generate this model is approximately ten hours to generate the training set of data, followed by less than 5 minutes to produce the neural network model. Each evaluation of the model then takes less than 0.5 s. Further work is needed to investigate the accuracy of the model over the whole design space, as it is important that there are not areas where there is large divergence from the correct data. This is a possibility, particularly at the extremities of the design space of the training set data. If this was the case, it may be prudent to increase the range of the input parameters used in the training set data, to cover a larger design space than will be evaluated using the approximation model. Refinement of the approximation model settings would also be possible, and may give even more improvement in the accuracy of the model. Assuming that the model fits the data well over the entire design space, the approximation model can then be used for optimisation and design sensitivity studies. It is estimated that the time to perform an optimisation will be reduced by at least 95% by using the approximation model, compared to using the complete RomaxDesigner analysis (not including the overhead of producing the training set of data - however, this is something that can be completed overnight with no user intervention). This reduction in run-time will be of great benefit to the user. It will enable the effects of more design parameters to be investigated, the effects of tolerances to be studied in more detail and ultimately lead to a better understanding of the overall behaviour of the system. Further work is needed to investigate the best method of selecting the training set design points. This is likely to become more critical when an analysis that takes significantly longer than the planetary TE analysis (5 minutes) is approximated, as fewer training set design points will be able to be analysed in a given time, or when more design variables are included.

CONCLUSIONS This paper has described an investigation into a number of approximation methods applied to a non-linear analytical model of a truck planetary hub reduction unit. The proprietary software package, RomaxDesigner has been described and the hub reduction unit modelled. The effective transmission error of the planetary set was then calculated using the software for a range of design variants to generate a set of "training data". Three approximation models were then fitted to this "training data" and the accuracy of the data, and the accuracy of each model assessed. The Neural Network model was significantly more accurate (around 1% error) than the Polynomial Cubic and the Radial Basis Functions models (4-6% error). More work is required to further validate the approximation models, but this initial investigation suggests that the Neural Network approximation method will be a useful tool for software packages such as RomaxDesigner.

16

USEFUL REFERENCES NISTISEMATECH e-Handbook of Statistical Methods :Engineering statistic (online) available from http://www.itl.nist.gov/div898Ihandbook/index.htm Practical experimental designs for engineers and scientists 3rd edition. William J Diamond Artificial neural network technology- introduction and purpose available from http://www.dacs.dtic.mil/techs/neural/neuraI1.html Crash introduction to artificial neural network by Ivan Galkin, U. MASS Lowell available from http://ulcar.uml.edu/~iag/CS/lntro-to-ANN.html International symposium and workshop advance training workshop Design exploration practical tips and trick. DR Therese Polito NAFEMS 2005: Modeling and analysis of a modem automatic transmission Gearbox Y Song. A Tylee-Birdshall, E Roeloffzen 'A software tool for Prediction of Planetary Gear Transmission Error', Dr Jamie Pears, Andrew Smith, Dr Sarah Curtis.

Table 1: Generation of the training data Parameter for modification

Range of modification

Sun gear Lead crown

±20um

Sun gear lead slope

±20um

Planet gear lead crown

±20 urn

Planet gear lead slope

±20um

Rotational position of planet gear one and three

± 0.5 degree

Radial Internal Clearance of needle roller bearings under planet gears

± 100 urn

17

Table 2: Observation value of transmission error and prediction model and percentage of error TE 1st Harmonic Prediction Validation point number

1 2 3 4 5 6 7 8 9 10

11 12 13 14 15 16 17 18 19 20

Value

%error

Approximation Radial Basis Function Value %error

Value

% error

0.83 0.58 0.82 0.75 0.85 0.56 0.97 1.02 0.6

0.8149 0.6602 0.8404 0.7716 0.892 0.6228 0.9488 0.9485 0.6086

1.82 -13.83 -2.49 -2.88 -4.94 -11.21 2.19 7.01 -1.43

0.827 0.6101 0.8473 0.7467 0.8541 0.5796 0.8868 0.9113 0.5657

0.36 -5.19 -3.33 0.44 -0.48 -3.5 8.58 10.66 5.72

0.8509 0.5913 0.8201 0.7589 0.855 0.5715 0.9735 1.015 0.6113

-2.52 -1.95 -0.01 -1.19 -0.59 -2.05 -0.36 0.49 -1.13

0.96 0.7 0.64 0.77 0.52 0.74 0.81 0.78 0.74 1.02

0.9262 0.7461 0.6448 0.8037 0.6039 0.8028 0.8559 0.8329 0.7035 0.967

3.52 -6.59 -0.75 -4.38 -16.13 -8.47 -5.67 -6.78 4.93 5.2

0.9249 0.7243 0.6507 0.7906 0.4843 0.7883 0.8361 0.7792 0.7361 0.932

3.66 -3.47 -1.67 -3.38 6.86 -6.52 3.22 0.1 0.53 8.63

0.9711 0.6971 0.634 0.7721 0.5364 0.7297 0.8168 0.7808 0.7398 1.009

-1.16 0.41 0.94 -0.27 -3.15 1.39 -0.84 -0.1 0.02 1.08

0.97

0.904

6.8

0.8792

9.36

0.9654

0.47

Calculation RomaxDesigner

Polynomial Cubic

Table 3: Measures of error in approximations Model Poly-cubic

RBF Neural net

Mean Absolute Error 5.9% 4.3% 1.0%

18

Std Dev Absolute Error 4.1% 3.3% 0.9%

Neural Network

GEAR TEETH IMPACTS IN HYDRODYNAMIC CONJUNCTIONS: IDLE RATTLE

o. Tangasawi l , S. Theodossiades l , H. Rahnejat l and P. Kelly 1 Wolfson School ofMechanical & Manufacturing Engineering, Loughborough University, Loughborough, UK 2 Powertrain Engineering, Ford Werke AG, Cologne, Germany ABSTRACT In the last decade, idle rattle in automotive transmissions has become a major concern in powertrain engineering. Palliative methods such as the Dual Mass Flywheel (DMF) have produced encouraging results in attenuating gear rattle with high cost implications. Fundamental and economic solutions can only source from root cause investigations. This paper introduces a new approach to study gear rattle from a tribo-impact perspective; gear impacts causing rattle are treated as lubricated conjunctions rather than following the conventional assumption of dry contacts. It was found that depending on the forcing conditions and the geometric/viscous characteristics of the system, various pairs of gear teeth could be excited, exhibiting a different system response. NOTATION C

Clearance between gear and shaft

I j (i=1.2.3.4.5.6) Moment of inertia of the i 'h gear I prey Moment of inertia of reverse pinion I wrev

Moment of inertia of reverse wheel

L

Length of contact line

II rc

Length of contact line in conformal contact between gear and shaft Radial distance of pinion and wheel contact point

req

The equivalent curvature radius of two teeth surfaces at their contact point

ros

Radius of output shaft Rolling velocity of gear teeth during meshing action

u Us

v an

f3

Sliding velocity of meshing gear tooth surfaces Tangential velocity between idle gear and supporting shaft Normal pressure angle

170

Pitch circle helix angle Dynamic viscosity

({Jj(i=2 .. 6)

Angular displacement of 2nd , 3"\ 41h, 51h and 6th idle wheels

't'1,prev

m

Angular displacement of the 1sl speed gear and reverse pinion

({Jwrev

Angular displacement of the reverse speed gear

(jJ,n(t)

Angular displacement of the input shaft

W Fj

Lubricant reaction Hydrodynamic load on the tooth flank

rw

Contact radii of gear wheels

rpm

Contact radius for the reverse speed pinion 19

TjW

Flank friction torque

I;ractw

Tractive torque between the output shaft and idle gear wheel Lubricant film thickness Petroff friction force

h F Subscripts P

prev w

wrev

Pinion Pinion of reverse gear Wheel Wheel of reverse gear

INTRODUCTION Idle gear rattle is associated with the characteristic noise that unselected impacting gears radiate to the environment (1). It is induced by engine order vibration in the presence of backlash in meshing pairs, particularly troublesome in vehicles with diesel engines (2). The system energy manifests itself in torsional vibrations at harmonics of the engine speed, a part of which is transmitted through the bearing mounts and transmission shafts to the gearbox housing and radiated to the environment as noise (2, 3). Transmission rattle has come to the forefront of noise and vibration issues for the automotive industry due to the attenuation of engine noise during the last decades (3). PalIiative measures have been extensively used to reduce the undesired effects of rattle. The increment of the engine idling speed (4), the use of dual mass flywheel system (5) and the tuning of the clutch pre-damper and hysteresis characteristics (4) are typical palliatives. Moreover, increasing drag torque has been found to be a low-cost option (6), which also increases the frictional losses and the transmission operating temperature. The use of backlash eliminators has the drawbacks of increasing friction losses and generating unacceptable heat (7). A number of investigations on gear rattle have been reported in literature, where the driveline has been modelIed as a lumped mass parameter system with hysteresis. The gear backlash is usualIy included using the dead space function (6) while the clutch stiffness and hysteresis have been modelIed as piece-wise linear functions (6, 8). Drag torque is usualIy added proportionalIy to the rotational speed (6) or temperature dependent (7). The gear tooth stiffness has been included as constant (6) or using timevarying contact stiffness coefficients (9, 10). GeneralIy, dry gear teeth impacts have been considered in rattle investigations. The effect of lubricant has not been included in fulI transmission models to keep the problem simple. Nevertheless, a lubricated contact model that assumes the presence of a hydrodynamic film in simplified non-varying gear teeth contacts was proposed by (11) for lightly loaded idle rattle conditions. Thus, a hydrodynamic rather than an elastic force is applied between the gear surfaces, which depends on the lubricant entrainment speed, the contact geometry, and the approach velocity between the teeth. This paper presents a numerical investigation of a front wheel drive six-speed manual transmission system under idle rattle conditions. The dynamic model includes the kinematics of teeth contact and their geometrical characteristics variation, hydrodynamic lubricant film formation and the effect of lubricant rheology. The numerical results are compared to experimental measurements from a vehicle and the main frequencies of the system response are identified, revealing the validity of the proposed methodology. 20

~

~

an

RaY.

~~nft.D"l.R""".l.~"~I~~

1

~\/ \\\

F; ~ 3d

\ \\F \

..:;/

~'i

-'/F; 5h

1111

rev

~~

-_

1st~stat.)..i)"-.D..l .....1/ F'z '"

'l

'"

\\\\

0dl

4h Figure 1: Front wheel drive transmission layout. ANALYSIS

The layout of the front wheel drive gearbox examined is shown in figure 1. An input shaft transmits the engine torque to the two output shafts and from there to the front wheels via the differential unit. The angular displacement
tp2

tp3

tp4

tps

tp6

tpwrev

Y. The motion of the 1

sl

speed gear

and reverse pinion is denoted by tpl,prev' tpwrev is the reverse speed gear motion and tp;(i~2.6)

correspond to the gears of the other pairs. The equations of motion are then formulated as follows for the idle gears mounted on the two output shafts: (II

+ I prey )tPJ,prev = FI (tpl,prev, tpin'
- Frev (tpwrev , tpl,prev'
= F 2(tp2,tp;n'
(2)

(3) (4)

= F4 (tp4' tpin'
(5)

I 6tP6 = F6 (tp6' tpin ,
(6)

14tP4 IstPs

I wrev¢wrev

=

Frev (lPwrev, fPI,prev' (Pwrev' ¢l,prev)rwrev

(7)

- Tfivrev (tpwrev, tpl,prev'
In the above equations,

F;(i~J.6,rev)

represent the hydrodynamic reactions between

the gear teeth meshing surfaces, rwi (i~J.6,rev) are the contact radii of gear wheels and rprev is the contact radius for the reverse speed pinion. The variations of the gears' geometric characteristics during the meshing cycle are calculated using the methodology described in (12). The flank friction force is given by T jivi (i~l...6,rev)' while the tractive force between the output shafts and the loose gear wheels is given by 21

J;raclwi(i~1

..

6,rev)'

In this investigation, iso-viscous rigid hydrodynamic conditions have been assumed between the gear meshing teeth due to the low loads experienced under engine idle conditions. The lubricant reaction, W, for such conjunctions was derived by Rahnejat (13) and also independently by Sasaki et al. (14):

2u -

fiE Vreq

~~I' ~~

<0

(8)

oh C. 0

ot

The lubricant film thickness between the teeth surfaces is obtained as:

h = Cb -Ir/p p

-

(9)

rwqJwl

The squeeze velocity with first order approximation is given as:

oh

Iho ld - hnewl

ot

M

where hold,

(10)

h new correspond to the previous and current time step, respectively. It is

noteworthy that a negative value for oh indicates mutual approach (squeezing) of the

ot

meshing gear teeth, whilst a positive value indicates separation, which does not contribute to the load carrying capacity of the contact zone. The action of the hydrodynamic load W is illustrated in figure 2. The load on each tooth flank is then given by: F; (;=1.6.rev) = W cos,B cos an (11) The hydrodynamic flank friction acts normal to the line of contact (figure 2) and has been derived from (15) using half-Sommerfeld condition: mJ 0 Ff

=

us";;::;

(12)

J2h

Therefore, the torque due to tooth flank friction is defined as:

= rcFf tan an

(13) cos,B The hydrodynamic tractive force induced by a film of lubricant between the output shaft's surface and the inside surface of the idle gear wheel resisting the motion (figure 2) is known as the Petrov friction (16). Assuming a concentric arrangement with zero eccentricity between the shaft and gear wheel due to low loads in idle conditions, the tractive force can be written in the following form: T

f

F

C The resistive torque generated is calculated as: I;ract = Fros

22

(14)

(15)

Contact lines /~---'r-..... "--- at different times during

(a)

meshing

rotation

Tooth flank

(b)

Shaft

, '"

, I

\

\

,

Lubricant between gear teeth surfaces Pinion

Figure 2: a) Entraining velocity and hydrodynamic friction on the tooth flank, b) Hydrodynamic force and Petrov's force. EXPERIMENTAL SET-UP The validation of the numerical model was completed using measurements of the transmission response from a vehicle equipped with diesel engine. The experiments were performed with the engine under idling condition (800rpm) and the transmission in neutral. The aim was to correlate the numerical results to the response of the transmission walls (due to limited access to the transmission shafts) and thus, to examine the accuracy of the torsional model, since it is well established that rattle noise is radiated to the environment due to vibration of the gearbox surface. Accelerometers were mounted to such locations, so that they monitor the path of vibration. Thus, vibration was measured from the corresponding bearing locations on the transmission housing, as well as from a less stiff region of the gearbox, as shown in figure 3. Two accelerometers were mounted upon the transmission wall, where the bearings of input shaft and the second output shaft are assembled (transducers [1] and [2] in figure 3), also indicated with white arrows in figure 3b. The third transducer ([3], indicated with black arrow in figure 3b) was mounted on the under-side of the transmission housing, where the wall is less stiff and, hence, more responsive to the excitation. Additionally, 23

[2] L...--Io---I [3]

(a)

Transmission rear view

Figure 3: a) Schematic diagram of the gearbox showing the positions of transducers and b) The transducers used on the vehicle gearbox. impact hammer testing of the gearbox housing revealed its natural frequencies by combination of the amplitude and phase graphs of the frequency response function. RESULTS - DISCUSSION The equations of motion are solved using the linear acceleration method for 30seconds of real time. The case examined corresponds to typical backlash values at the various gear pairs (varying from 80 - 150/-lm), lubricant dynamic viscosity for a "cold" transmission (0.151 Pa·s at 20°C) and engine speed in idle (800 rpm). The FFT spectra of the gears' angular accelerations are shown in figure 4. The idle gear pairs with lower inertias (3rd, 5th and 6th) exhibit higher amplitudes of oscillations and larger spectral content compared to the high inertia pairs (1 51 and 2nd). This is an indication that rattle should be expected mostly from the 3rd - 6th gear sets. As it can be seen, engine order vibration is an important source of excitation, leading to torsional oscillations of the loose gears with the various orders up to and including the 10th order (:::0135 Hz) being present in the spectra. The most significant is the second engine order (:::026 Hz) with other main contributions at 4th (:::052 Hz), 6 th (:::080 Hz) and 8th (:::0106 Hz) engine orders. 24

26 52 5 10

15

lal

237 595

12.5

26 52 106

575

Ibl

357 10

",4

1j

i

7.5

-1)3

'"

f

f2

5

1000

2000

3000

4000

Frequency (Hz)

25 26 52 106

900

lei

I

600

20

~15

.

'"'"

1495

~

:[

!

~

flO

f

,JII~~t, d" 0 0

J 1000

.1

I 2000

k. 3000

4000

Frequency (Hz)

lei

26 52 106

77B

10

4000

Figure 4: FFT spectra of the idle gears' rotational accelerations (a) 1st gear, (b) 2nd gear, (c) 3rd gear, (d) 4th gear, (e) 5th gear and (f) 6th gear.

Comparison of these graphs to the ones obtained from vehicle measurements (figure 5) reveals a strong presence of the engine orders in the transmission less stiff wall but not high influence in the bearing areas. The bearing mounts are absorbing a large portion of the low frequencies' contribution but clearly permitting higher frequency bands, as shown in graphs 5b and c. The main common frequencies observed in the spectra of figures 4 and 5 other than the engine orders are the following: 357 Hz, 550-575 Hz, 590-605 Hz, 635 Hz, 720 Hz, 770-805 Hz, 900 Hz, 1020-1030 Hz, 1780-1810 Hz and 2530-2580 Hz. The appearance of those frequencies in the spectra can be generally attributed to three possible sources. The first source is the meshing frequency of each gear pair. The 1st (175 Hz), 2nd (293 Hz), 3rd (386 Hz), 4th_6 th (506 Hz), 5th (472 Hz) and reverse (l08 Hz) gear meshing frequencies and their multiples are present in the above spectra in both high and lower 25

0.9

26 52 106

(aJ

0.8 0.7

1jO.5 06

~O.4

'" 0.3

3380 357

0.2 0.1

Frequency (Hz)

Figure 5: FFf spectra of the transmission pick-up points (a) bottom wall, (b) input shaft bearing and (c) upper output shaft bearing. frequency ranges, revealing strong interactions between the various gear sets. For example, in figure 4f the 1031 Hz and 2576 Hz peaks correspond to the 2nd and 5th orders of the 6th gear pair meshing frequency. The second source is the natural frequency of each loose gear, which has been calculated by linearization of the set of equations (1) - (7), including also the equation of motion of the input shaft. In the linearised equations of motion, the lubricant stiffness is determined by differentiating equation (8) with respect to the film thickness, considering the rolling term only:

K(~J=I~~I

(16)

The solution of the eigenvalue problem determines the natural frequencies of the system, which are: 576 Hz (1 st gear), 488 Hz (2 nd gear), 905 Hz (3 rd gear), 726 Hz (4th 26

gear), 2783 Hz (5 th gear), 1229 Hz (6 th gear) and 293 Hz (reverse gear), explaining some of the main peaks observed in the spectra of figures 4 and 5. Still a number of contributions appear in the spectra of the measurements (i.e. 980 Hz, 2200 Hz, 3380 Hz), which do not correspond to engine orders, gear meshing frequencies and their multiples or the natural frequencies of the torsional model. The natural frequencies of the gearbox case are the third source, responsible for those contributions. Impact hammer testing of the case has revealed its main natural frequencies, which are the following in the frequency band of interest up to 4 kHz: 1020 Hz, 1200 Hz, 1430 Hz, 1750 Hz, 1845 Hz, 2070 Hz, 2210 Hz, 2330 Hz, 2700 Hz, 2810 Hz, 2890 Hz, 2950 Hz, 3065 Hz, 3185 Hz, 3385 Hz, 3625 Hz, 3740 Hz and 3950 Hz. Remarkably, some of those gearbox frequencies are very near to multiples of the gear meshing frequencies and therefore, are excited from the torsional vibrations of the idle gears. For example, 2070 Hz is multiple of the 4th and 6th gear meshing frequencies, which are mounted on the upper (second) output shaft. This observation is very important for the identification of the main sources responsible for rattle vibrations.

CONCLUSIONS A torsional model of a front wheel drive manual transmission has been developed, including the gears' meshing cycle characteristics, the lubricant effect, friction between the gear flanks and the drag in the gear wheels-shafts mounts. The numerical results have been validated by comparing the peak frequencies observed in the FFT spectra with gearbox vibrations measured on a vehicle under similar idle conditions, as presented in figures 4 and 5. The intention of the authors was to identify the limits of a purely torsional model of the gearbox on the prediction of the main rattling frequencies. Strong interactions have been identified in the spectra of the various gear pairs. The model predicts accurately most of the frequencies that appear in the measurements, which result either from the natural frequencies of the idle gears or the meshing frequencies of gear pairs and their multiples. However, it cannot predict the input/output shafts' bearing reactions, which are eventually the main excitation terms of the gearbox vibration. This is the reason for the differences observed between the numerical results and the experimental measurements. A model including the capability of the input/output shafts' lateral motions is the subject of future work and is expected to shed light on the direct interactions between the gearbox housing and the shafts' torsional vibrations.

ACKNOWLEDGEMENTS The authors wish to express their gratitude to Ford Motor Company for sponsorship and financial support extended to this research project.

REFERENCE LIST 1 S. N. Dogan, G. Lechner, 'MaJ3nahmen zur Verringerung von Losteilschwingungen in Fahrzeuggetrieben', ATZ Autombiltechnische Zeitschriji100 (1998) 10,710-716. 2 T. Sakai, Y. Doi, K. Yamamoto, T. Ogasawara, M. Narita, 'Theoretical and Experimental Analysis of Rattling Noise of Automotive Gearbox', SAE Technical Paper 810773 (1981), 1-10. 3 M. Wang, R. Manoj, and W. Zhao, 'Gear Rattle Modelling and Analysis for Automotive Manual Transmissions', Proc. Instn. Mech. Engrs. Part D, (2001) 215, 241-258. 27

4 R. Seaman, C. Johnson, and R. Hamilton, 'Component Inertial Effects on Transmission Design', SAE Technical Paper 841686 (1984), pp. 6.990-6.1008. 5 G. Fudala, T. Engle and A. Karvelis, 'A System Approach to Reducing Gear Rattle', SAE Technical Paper 870396 (1987), pp.2.l10-2.117. 6 T.C. Kim, R. Singh, 'Dynamic Interactions between Loaded and Unloaded Gear Pairs under Rattle Conditions', SAE Technical Paper 2001-01-1553 (2001),1934-1943. 7 T. Fujimoto, and T. Kizuka, 'An Improvement ofthe Prediction Method of the Idling Rattle in Manual Transmission- In the Case of the Manual Transmission with Backlash Eliminator', SAE Technical Paper 2001-01-1164,2001. 8 R. Singh, H. Xie, and R. Comparin, 'Analysis of Automotive Neutral Gear Rattle', Journal of Sound and Vibration (1998),177-196. 9 A. Kahraman and R. Singh, 'Interactions Between Time-Varying Mesh Stiffness and Clearance Non-Linearities in a Geared System', Journal of Sound and Vibration (1991), 146(1),135-156. 10 S. Theodossiades and S. Natsiavas, 'Non-Linear Dynamics of Gear-Pair Systems with Periodic Stiffness and Backlash', Journal of Sound and Vibration (2000), 229(2), 287-310. 11 M. Gnanakumarr, S. Theodossiades, and H. Rahnejat, 'The Tribo-Contact Dynamics Phenomenon in Torsional Impact Of Loose Gears - Promoting Gear Rattle', SAE 02ATT-138, Society ofAutomotive Engineers (SAE)-ATT Congress, Paris, 2002. 12 O. Tangasawi, S. Theodossiades, H. Rahnejat, P. Kelly, 'Gear Teeth Impact Dynamics in Manual Transmissions Promoting Idle Rattle', Proceedings of the 5th EUROMECH Nonlinear Dynamics Conference, Eindhoven, 2005. 13 H. Rahnejat, The Influence of Vibration on the Oil film in Elasto-hydrodynamic Contacts, PhD Thesis, Imperial College, University of London, 1984. 14 T. Sasaki, H. Mori and N. Okino, 'Fluid Lubrication Theory of Roller Bearings', Trans. ASME, J. Basic Engineering, 1962. 15 R. Gohar, Elastohydrodynamics, Imperial College Press, 2 nd edition, 2001. 16 B. Hamrock, S. Schmid, and B. Jacobson, Fundamentals of Fluid Film Lubrication, Marcel Dekker Inc., 2 nd edition, 2004. © Loughborough University

28

THE PREDICTION OF LOADED STATIC TOOTH BEHAVIOUR FOR AUTOMOTIVE PARALLEL AXIS GEARS Adrian Leavitt - Research Postgraduate School ofMechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK Dr. Peter Brooks - Senior Lecturer School ofMechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK David Parkin-Moore - Principal Design Analyst Ricardo UK Ltd, Southam Road, Radford Semele, Leamington Spa, CV31 1FQ, UK A series of enhancements have been made to a thin-slice gear model originally proposed by Smith [1]. The model predicts Transmission Error (TE) under conditions that simulate an equivalent static load across the mesh of external helical gear pairs. Dynamic effects are not taken into consideration. An mathematical function derived by Cai [2] is incorporated to represent the variation in mesh stiffness throughout the mesh cycle. The model provides a rapid assessment of the effects of macro and micro geometry on TE. Several case studies have been conducted and the results have been verified against experimental data. NOTATION 1,2

i,j ,k

Xamax Xf3max XYmax Ypb Yk

Jj y,z f3 f3b lia lip liy Pmin

Pmax

Subscripts to denote wheel and pinion gears respectively Subscripts to denote contact point, contact line and calculation step respectively Overlapping face width [mm] Outer diameter of wheel / pinion [mm] Relief start diameter of wheel/pinion [mm] Pitch diameter of wheel / pinion [mm] Base circle diameter [mm] Base pitch [mm] Calculation steps per base pitch of rotation Number of slices across face with Number of contact lines considered in model Amount of relief specified at tip [mm] Amount of crowning specified at centre of face width [mm] Amount of misalignment [mm] Displacement iny due to helix angle [mm] Displacement in y due to rotation at step k [mm] Offset due to position of line j relative to pitch line [mm] Co-ordinates in pressure plane Helix angle (without subscript: at reference diameter) [deg] Base helix angle [deg] Transverse contact ratio Helical overlap ratio Total contact ratio Roll distance from pitch point to start of relief [mm] Roll distance from pitch point to tip [mm]

29

Matrices Y ij Distance of point i on line} from pitch line (y=0) [mm] YO ij Nonnalised distance iny from pitch line [mm] Xaij Instantaneous amount of relief [mm] XPij Instantaneous crowning [mm] XYij Instantaneous misalignment [mm] Oij Total interference [mm] INTRODUCTION Transmission Error (TE) is described as, "the deviation in the position of the driven gear (for any given position of the driving gear), relative to the position that the driven gear would occupy if both gears were geometrically perfect and undefonned" [3]. It is widely attributed to the deflection of gear teeth under load, which in tum is influenced by variations in mesh stiffness as the gears rotate. Mesh stiffness is the combined stiffness of the mating teeth and is defined as the force along the line-of-action divided by the resulting mesh deflection. Researchers have been striving to model the compliance of a gear mesh since the early 20th century. The earliest approaches modelled the gear tooth as a cantilever beam. A model of this nature was developed by Timoshenko [4] . Weber [5] proposed a strain-energy method to obtain analytical expressions for predicting tooth deflection and integrated the actual tooth profile. Walker [6] improved these models by incorporating empirical equations to describe tooth deflection. Walker also illustrated the manner in which load distribution varied during different phases of gear rotation. More recent approaches have involved the development of extensive finite element models to model the contact conditions to a high degree of accuracy [7]. Recognising that tooth deflection exceeded manufacturing errors, Walker [6] proposed a series of modifications to the tips of mating spur gear teeth to compensate for deflection effects. The resulting "tip relief' was shown to eliminate the abrupt changes in tooth load and angular velocity at the transition points between single and double tooth pair contact. The amount of relief was carefully chosen to compensate for a specific amount of deflection. Harris [8] later developed a graphical technique to demonstrate variation in TE with load, highlighting that for spur gears, minimum TE could only be achieved for a single loading condition. The purpose of this work is to develop a model capable of rapidly predicting the contact tooth behaviour of helical gear pairs. As such, the model concentrates solely on the behaviour of the mating gear pair, assuming it is rigidly supported. The thin-slice modelling approach has been adopted and the basis for the model was taken from work by Smith [1]. Smith's original model assumed a constant value of mesh stiffness and at the outset of this work it was identified that this assumption could jeopardise the accuracy of the TE predictions. Therefore, a representation of the mesh stiffness variation has been incorporated into Smith's original model. Rather than resorting to computationally demanding finite-element techniques it was decided that an analytical equation would be used instead. In order to assess the validity of this stiffness variation equation, a series of case studies have been conducted and the results compared firstly against Smith's original approach, and secondly against experimental data from a dedicated test facility.

30

DESCRIPTION OF THIN-SLICE MODEL Modelling assumptions

The helical tooth is represented as a series of 2-dimensional slices which are staggered to cater for the helix angle. The slices are made in the transverse plane and it is assumed that each slice is restrained axially by its neighbour. One argument against this method is that in reality, if a load is applied to an individual slice, the neighbouring slices would support or "buttress" the slice due to shear stresses and longitudinal bending stresses. Also, at the end of the tooth, the stiffuess would be significantly lower due to the fact there is no support on one side. However, accurately machined teeth do not have sudden changes of loading along a line of contact. In general, the load increases gradually as the teeth engage and remains constant over a large section of the line of contact. Neighbouring slices exhibit similar loads and deflections and therefore the shear buttressing effects should be small. With smooth load increases or decreases the shear force effects on either side of a slice will cancel each other out [1]. Calculation method

Contact position All calculations within the thin-slice model are referenced to the theoretical twodimensional plane of action, as opposed to the actual three-dimensional geometry of the teeth. The plane of action lies at a tangent to the base cylinders and extends across the overlapping face width of the mating tooth profiles. On any given tooth flank, contact can only occur on a single contact line. In the case of spur gears, this contact line runs in the axial direction across the tooth face. With helical gears, the contact line is inclined at an angle fib to the axial direction, where fib is the base helix angle. Typically, contact may exist on previous or later tooth flanks which are still within the contact zone, and these need to be catered for in the analysis. WHEEL i !

Limit of pinion tip contact

-I-tL+

! !

T

I

i

--'-'_'"~

Transverse meshing length

1

Pitch Line

i

, ;

;

-i-i-

J7~l-~

Limit of wheel tip contact

i

l ____ .... _, __ L !

! ; ; ;

i

i

PINION

Face width divided into N slices

~I

Figure 1 Pressure plane shown with central contact line in datum position (modified from [1])

31

The point PP in Figure 1 is the pitch point. A contact line is shown to pass through PP mid-way through its length. This is the datum position and all measurements relating to contact position are referenced to this datum. The additional contact lines which exist ahead and behind the central line are also considered. The precise number of lines considered is denoted by L. The model calculates the contact position up to a maximum of one base pitch ahead and behind this datum position. The contact conditions are calculated at P steps per base pitch (i.e. 2P steps taken in total), where P is specified by the user at the start of the analysis. At each step k, the global position matrix Y ij is constructed to describe the position of each potential contact point relative to the datum line: Yjjk = 1'; + Yj + Yk

1';

=

{for i = 1, ... ,N, j = 1, ... ,L, k = 1, ... ,2P}

tan(Pb) x Zj

(1) (2) (3)

Yk = ~(k-P) P

(4)

The potential contact points are calculated for each contact line in turn and consider the effect of the helix angle (1i), offset due to contact line position (.Y;) and mesh progression (Yk ).

Micro-geometry profile corrections Tip relief corrections for an individual slice depend upon the distance Y ij of the potential contact point from the pitch line. When plotted against roll distance, tip relief appears as a linear deviation from the true involute line (Figure 2). The upper portion shows the individual tip relief profiles applied to the pinion and wheel teeth. The resulting combined tip relief is shown in the lower portion of Figure 2.

Figure 2 Modelling of tip relief throughout a single mesh (modified from [1]) 32

The amount of relief Xa. for any given position Y is given by:

{for i

= 1, ...,N;j = 1, ... ,L} (5)

Xamax is the amount of tip relief applied to the gear, Pmin is the roll distance between the pitch point and the start of the tip relief and Pmax is the roll distance between the pitch point and the tip of the gear. Figure 2 illustrates the case where an element ofY is positive. In this instance, pinion geometry data would be supplied to Eq. 5. If a given contact position has a negative value, this indicates contact is approaching the tip of the wheel and the geometry of this gear is applied to Equation 1.5 instead. Equation 1.5 is a modified version of the approach originally proposed by Smith [1], and allows tip relief to be specified at the tip of the gear rather than at ±O.5Pb from the pitch point. It is believed that this information is more readily available from a gear drawing and therefore less calculation effort is required. The corresponding roll distances are calculated using the tip (Do), relief start (D st ), pitch (Dp) and base circle (Db) diameters: Pmin

=

Pst - Pp

(6) Prnax

=

Po - Pp

(7) Crowning and lead correction are also catered for in the model. The crowned profile is represented as a parabolic curve: 2 ) x Plj.. =xPm" ( ~ O.Sb

{jor i = 1, ... ,N}

(8)

Where XfJrnax is the applied crowning relief at the edges of the tooth width. The amount of lead correction is solely dependant on the axial distance along the face width z:

x Yij -x rm~ (~) b

{jor i = 1, ... ,N}

(9)

Interference and contact force Once the potential contact positions and corresponding amounts of profile corrections have been determined, the total interference 0 at each point is calculated:

33

(10) Where Xp is the interference at the pitch point. This value is initially assigned an arbitrary magnitude of le-6. Later in the calculation process, this value is refined during an iterative process which compares the predicted contact force against the applied load. The contact force is calculated on each slice using the calculated interference values and assuming a value of tooth stiffness. The values of each slice are then summed together to determine the overall contact force. Together with an estimated value of overall mesh stiffness, this value is compared against the applied load. Any force discrepancy is then accounted for by adjusting the value of~. The interference and contact force calculations are then repeated using the amended ~ value. This iterative process continues until the predicted force corresponds to the applied load within a specified tolerance limit.

Mesh stiffness function The disadvantages associated with the earliest modelling approaches relate to assumptions regarding tooth thickness and width. Traditionally, the thickness was assumed constant and the width infinite. Additionally, the load was assumed to act parallel to the tooth tip. However, these assumptions are only valid when dealing with spur gears, and negate the important effects encountered in helical gearing. In a series of work, Umezawa [9-12] developed analytical equations capable of predicting the deflection a helical gear tooth. The finite differences method was used to develop a series of effect functions to describe the reaction to a concentrated load at discrete points on the tooth surface. The analysis catered for tooth bending effects and local contact deformation. The effect functions were solved simultaneously to calculate the load distribution. This methodology was later refined into a series of approximate equations which captured the key elements of the original more rigorous approach. Cai [2] later extended Umezawa's approximate equations to include the effect of tooth numbers and addendum modification coefficients. The stiffness function used to describe a single helical tooth pair is as follows (modified from [2]):

(11)

Where kp is the stiffness at the pitch point, Ga is the transverse contact ratio and Gy is the total contact ratio. kp, together with the coefficient Ca , require calculation beforehand in an identical manner to Cai [2]. Individual tooth stiffness curves are combined together to generate the overall mesh stiffness curve.

INVESTIGATION INTO EFFECTS OF TOOTH DEFLECTION ON MESHING BEHAVIOUR In order to assess the improvements made to the model, a number of case studies have been conducted. These studies have been designed to reflect earlier experimental work, conducted by the Driveline Research group at the University of Leeds in conjunction

34

with Ricardo UK Ltd., to provide a direct means of validating the predicted results. Details of the dedicated test facility used in the experimental investigation have been published in an earlier pUblication [13]. The studies focus on analysing the TE of a gear pair over a wide range of applied input torque. In addition, the effect on the meshing behaviour of altering the transverse contact ratio Sa and helical overlap ratio sp is examined. Inclusion of mesh stiffness variation

The influence of including the mesh stiffness function into the model has been assessed by comparing the outcome against TE predictions calculated using a constant value of mesh stiffness. In addition, the results have been compared against equivalent experimental data. The geometry of the meshing gear pair featured on the dedicated test facility has been replicated within the model. Predictions for TE were calculated for a range of input torque values varying from 10 to 115 Nm (Figure 3). The TE predictions from the two simulation approaches in Figure 3 are similar in magnitude to one another. However, the inclusion of the Cai stiffness function has produced an important trend in the TE curve, whereby the minimum value ofTE occurs at 30-40 Nm, and either side of this range TE increases in a linear manner. This trend agrees with the findings of the experimental investigation and is a significant result since it reflects a feature of the gear design. A small amount of tip relief and crowning has been applied to the tooth profiles of the gears used in this investigation to improve the meshing conditions. However, since tooth deflection will vary with applied input torque, the design can only be most effective at one particular load. In this instance, the overall design appears to cause minimum TE at an input torque of 30 Nm.

3.00 2.75 2.50 2.25 2.00

E

..::!.

1.75

UJ

1.50

e..

1.25

l-

~

1.00 0.75 0.50 0.25 0.00 0

20

40

60

80

100

120

Input torque [Nm]

Figure 3 Variation in peak-to-peak TE with increasing torque for gear pair with Sa equal to 1.733.

35

Effect of varying contact ratio on TE

There is substantial evidence to suggest that increasing the contact ratio of a gear pair will cause the teeth to mesh more smoothly with less load variation, resulting in lower TE. More recently, researchers have discovered that designing an integer contact ratio is more beneficial than simply increasing the magnitude. An integer value of contact ratio causes the sum of the lengths of the lines of contact to remain constant, resulting in a constant value of mesh stiffuess [14]. Since the deflection of a pair of teeth is also constant throughout the meshing cycle, the end result is a constant value of TE for a range of working loads. Helical gears possess a helical overlap ratio sp and transverse contact ratio Sa. Past research has examined whether Sa, Sp or even Sa + Sp (Sy) should be integer values [15, 16]. Further investigation into this phenomenon is presented here in an attempt to clarify which contact ratio should be prioritised. Table 1 Geometry of five gear pairs used in contact ratio investigation Geometry

A

B

C

D

E

14.0 74.7/ 64.0

39/33 33 14.0 73.3 / 62.8

17.3 75.0/ 64.3

17.3 74.7/ 64.0

2.13 2.00 4.13

2.00 2.00 4.00

Z1/Z2

jJ(deg) b(mm) Do/do

(mm)

14.0 75.0/ 64.3

Dp/dp (mm) Db/db (mm) Sa Sa Sy

70.4 / 59.6 66.7/56.4 2.13 1.62 3.75

2.00 1.62 3.62

1.45 1.62 3.07

Five different gear pairs have been tested over the same range of torque values considered in Section 3.1. The geometry of the five gear pairs is detailed in Table 1 above. Geometries B-E represent all possible combinations of integer contact ratio. Geometry A has a non-integer contact ratio and is used as a benchmark from which any improvement from the other gear pairs can be gauged. Alterations in Sa are achieved by reducing the tip diameter of the teeth, whereas alterations in sp are achieved by adjusting the overlapping face width b. The findings of the investigation (Figure 4 below) confirm that the reduction in TE over the greatest torque range occurs when both Sa and sp are an integer value. However, a minimum TE point in the region of 30 Nm no longer occurs. Instead, a gradual reduction in TE begins at 40 Nm and continues throughout the remainder of the torque range. Interestingly, when only sp is an integer value, the reduction in TE spans the same range of torque, albeit with an overall TE magnitude marginally greater than when Sa is an integer value as well. With only Sa set to an integer value, the improvement in TE is far less apparent. The only observable alteration to the TE curve is a reduction in the torque value at which minimum TE occurs. Below this torque value, the predicted TE is lower than the values obtained with the benchmark geometry. However, above this torque value, an overall increase in TE is obtained compared to the benchmark geometry. 36

1.4 1.2 1.0

E

0.8

2. W f- 0.6

a.. .9 a..

0.4 0.2 0.0

o

20

40

60

80

100

120

Input torque [Nm]

Figure 4 Variation in peak-to-peak TE for gears with different combinations of integer contact ratio CONCLUSIONS

A model capable of predicting TE under loaded conditions has been developed. The contact algorithm has been modified to include mesh stiffuess variation. Clear benefits of including this variation have been portrayed and the results feature important trends found within the experimental data. A discrepancy in TE magnitude has been observed and is attributed to the additional compliances (due to shafts and bearings) which influence the measurements from the dedicated test facility. Investigation into the use of integer contact ratios has also been conducted. An integer value of the total or helical overlap contact ratios will reduce TE over a wide torque range, whereas focusing on the transverse contact ratio offers little benefit. The most significant improvements are observable when the total contact ratio is maximised and both contact ratios are integer values. In future work the effect of misalignment will be considered when examining the influences of integer contact ratios on TE. It is recognised that compensating for increases in misalignment with torque may be more efficient means of reducing TE over a desired torque range.

37

REFERENCES 1. J.D. Smith, Gear Noise and Vibration, Marcel Dekker, Inc., New York, ISBN: 08247-6005-0, 1999 2. Y. Cai, Simulation on the Rotational Vibration of Helical Gears in Consideration of the Tooth Separation Phenomenon (a New Stiffness Function of Helical Involute Tooth Pair), Transactions of the ASME, Vol. 117, pp. 460-469, 1995 3. R.G. Munro, A Review of the Theory and Measurement of Gear Transmission Error, Proceedings of the Institution ofMechanical Engineers, C404/032, pp. 3-10, 1990 4. S. Timoshenko and R.V. Baud, Strength of Gear Teeth Is Greatly Affected by Fillet Radius, Mechanical Engineering, 48 (11), pp. 1105-1109, 1926 5. C. Weber, The Defonnation of Loaded Gears, and the Effect on Their LoadCarrying Capacity, Department of Scientific and Industrial Research, Sponsored Research (Germany), Report No.3, 1949 6. H. Walker, Gear Tooth Deflection and Profile Modification, The Engineer, pp. 409412;434-436,1938 7. J. Wang and I. Howard, Finite Element Analysis of High Contact Ratio Spur Gears in Mesh, Journal of Tribology, 127 (3), 469-483, 2005 8. S.L. Harris, Dynamic Loads on the Teeth of Spur Gears, Proceedings of the Institution ofMechanical Engineers, Vol. 172 (2), pp. 87-112, 1958 9. K. Umezawa, Deflections and Moments Due to a Concentrated Load on a RackShaped Cantilever Plate with Finite Width for Gears, 15 (79), 116-130, 1972 10. K. Umezawa, Meshing Test on Helical Gears under Load Transmission - 1. The Approximate Fonnula for Deflections of Gear Tooth, 15 (90), 1632-1639, 1972 11. K. Umezawa, Meshing Test on Helical Gears under Load Transmission - 2. The Approximate Fonnula for Bending-Moment Distribution of Gear Tooth, 16 (92), 407-413, 1973 12. K. Umezawa, Meshing Test on Helical Gears under Load Transmission - 3. The Static Behaviours of Driven Gear, 17 (112),1348-1355, 1974 13. D. Parkin-Moore et al., Development ofa Simulation Tool for the Prediction of Dynamic Transmission Error, the Source of Transmission Whine, In Multi-Body Dynamics - Modelling and Simulation Techniques III, Loughborough, 2004 14. D.B. Welbourn, Fundamental Knowledge of Gear Noise - a Survey, In Noise and Vibration ofEngines and Transmissions, !MechE Conference Publications, pp. 9-14, 1979 15. M. Choi and J.W. David, Mesh Stiffness and Transmission Error of Spur and Helical Gears, SAE Technical Paper Series, 901764, 1990 16. J. Esaki, Influences of Contact Ratios on Vibration and Noise of a Helical Gear System, In JSME Int. Conf on Motion and Power Transmissions, Hiroshima, Japan, pp. 66-71,1991

© Ricardo UK Ltd.

38

Boundary lubrication film formation from belt type CVT fluids Keiichi NARITAa.b, Martin PRIEST b aLubricants Research laboratory, Idemitsu Kosan Co., lid 24-4 Anesakikaigan, Ichihara-sh~ Chiha,299-0107, Japan bInstitute of Tribology, &hool of Mechanical Engineering, University if Leeds Woodhouse lane. Leeds, LS2 9JT, UK The influence ofme1al-me1altribological properties on the 1rnnsmittable tOIque capacity ofa me1al V-beh type con1inuously variable transmission (B-CVI) was experimen1aIly investigated using a cvr bench test and a 1ribometerwhich enables the fihn fonnation to be monitored during rubbing tests. As aresuh, the contact regions between the belt and pulley were found to be in the boundaiy lubrication regime and it is vital fur higher tOIque capacity to give higher fiiction coefficient and to demonstrnte good fihn fonnation between the contacting interfuces. Furthermore, the process of boundaiy lubricant fihn fonnation produced fium anti-wear additives used in B-CVT fluids should strongly impact upon the torque capacity.

NOTATION Suffixes p primary (drive)pulley s sewndmy (driven)pulley upper part ofthe beh 2 lower part ofthe beh C compressive fi>rre in the beh (N) CR contact resistance ratio ( - ) E' effective elastic modulus (Pa) F nonnalload between beh block side and pulley surfuce (N) F"dX axial force on the secondmy pulley (N) L beh block length in radial direction (m) I speed ratio ( -) M rmx. torque, 1rnnsmittable tOIque capacity (Nm) m .. m b mass per unit length ofa block and a band, respectively(kglm) N nonnalload between block shoulder and innenrost band in the arc part ofthe beh (N) Prr<m mean contact ~ between block side and pulley surfuce (Pa) R a composite surfuce roughness oftwo surfuces (m) R p radius to rocking edge ofblocks on the primary pulley (m) R py CUIVattn"e ofradius on pitch in the primarypulley (m) t beh block tbickness(m) SR slip ratio (%) T ring tensions (N)

V velocityofbehassembly(mls) Va relative slipping tangential velocity ofblock side to pulley surfuce (mls)

39

vcct1a:t

output potential across the contacting surface during the rubbing tests (mV)

a angle shown in Fig.3 (0 ) oil viscosity (pa' s) A halfthe pulley wedge angieC ) Q pulley rotated speed(IpIll) JI a coefficient of friction between beh block side and pulley surface Jib coefficient of friction between belt shoulder and innennost band ,Ii composite coefficient offriction between belt block side 1]

1

Introduction

There is a gradual increase in the number of new vehicles fitted with automatic transmissions (Ms) in Europe, reaching 20 % in 2003 [I] compared with less than 10 % at the beginning ofthel990s. Ms may be divided into two groups according to whether speed ratio changes are controlled by using gears or gear-less devioes. The first group is called a step-type AI; which use sets of planetaIy gears and shift clutches as a speed change device. An example of another type is the pushing metal V-belt type continuously variable transmission (CVI). The use of CVTs has been growing because of their good performance in tenns of driving comfort and lower fuel consumption in comparison with a four speed step-type AT. Regarding lubricants for CVTs, automatic transmission fluids (MFs) were used for both Ms and CVTs until the middle of 1990s. With the spread of application to larger vehicles, the most important performance aspects of belt CVT fluids (CVTF) is achieving greater transmittable torque capacity (higher friction coefficient) and preventing wear between the beh and pulley system of the CVT [2]. Zinc dithio phosphate (ZnDTP) additives could be cffective for improving the torque capacity of the CVT [3]. Therefore, ZnDTPs have been predominantlyused in CVTF. ZnDTPs have been widely used in engine oils since the 1950s [4] and many studies of the friction and wearperformanoes ofanti-wear additive ZnDTPs have been reported. The physical and chemical analysis ofboundaty lubrication films fum oil additives is being continually progressed [5]. Such studies have paid attention to the post test tnbofilm properties derived from ZnDTP but they did not refer to lubrication film formation during the rubbing prooess. The decomposition products generated from ZnDTPs may lead to the durability of wet clutches installed in CVT unit being deteriorated Therefore, a variety of additive fomrulation technology for the improvement of torque capacity is required. This study intends to examine the friction characteristics on the transmittable torque capacity of a metal V-Belt type CVT and to investigate the effects of anti-wear additives used in MFs and CVTFs, for example, arnnge ofZnDTPs and phosphorus compounds.

2 Description of a metal V-belt type cvr Fig.1 shows the general assembly of a metal pushing V-belt CVT [6, 7], which consists ofthe essential parts of two V-shaped pulleys and a steel belt The torque from the engine is transmitted to the driven secondaty pulley through the steel belt placed around the driving primaIy pulley. The belt assembly consists of about 400 flat blocks and thin laminated band sets of 9 or 12 sheets, which are set into the blocks on the right and left side. Contact between blocks as the belt moves around a pulley occurs through

40

a line contact parallel to the pulley axis called the rocking edge. The distance R p from the centre of the pulley axis to the rocking edge ofthe block on a pulley is defined as a pitch, as shoV\ll1 in Fig. I.

3

Experimental procedure

3.1 CVT bench test

Parameters influencing the transmittable torque capacity of the cvr were investigated using a cvr bench test as descnbed previously [S]. The belt assembly and the pulleys were taken out of a commercial cvr unit designed for a vehicle with a 2.0 Q engine. Torque capacity tests were carried out vruying the clamping force on the secondary pulley fum IO.S to 34.6kN (hydraulic pressure from O.S to 2.6MPa) and oil temperature from 40 to 100°C. This clamping force condition is in the range of actual nnming. Speed ratio 1is defined by (1)

Increasing the drive torque with a constant speed ratio grndually causes slip to 0CClU' between the belt and the pulley. The slip value is defined by the slip ratio, SR

SR=(l-~)x100

%

(2)

°s,n%ad

Slip ratio gradually increases with the increase of drive torque up to the mid-runge. When the drive torque reaches a limit torque, a macro slip suddenly occurs and no more torque can be trnnsmittcd. The torque capacity of test samples is defined as the drive torque at the moment when the slip ratio reaches a specified value. Study of torque capacity with lubricants should focus on a lower speed ratio condition because it gives a smaller value than that at higher speed ratio under the same secondary pulley force condition Measurements oftorque capacity were conducted at the lowest speed ratio of2.36. 3.2 Ball on plate tribometer

Tnborneters simulating the fiction behaviour under a relatively high contacting pressure such as between the belt and pulley have been proposed [3, 9]. In this study, a ball on plate reciprocating tnbometer [P1int77] was used, which enables lubricant fihn formation to be monitored during rubbing tests. A schematic ofthis machine is shoV\ll1 in Fig2. In order to monitor the lubrication fihn formation during rubbing tests, the contact resistance method was used. A DC voltage is connected across a potential divider, which is composed of 60 kn anQ 600Q resistance [10]. The lower resistance was connected in parnllel with the specimen contact The output voltage across the contact represents the contact resistance. This value is defined by contact resistance ratio, CR %

(3)

=

Where V is the output potential across the contacting surfiIce during the rubbing tests and a potential ofSOmV during the friction test means that the contacting pair is completely separated by a lubricants film. The test ball was the same material (Bearing steel) and hardness CHRcSS) as the belt block and the test plate was the same material (Orromium steel) and hardness CHRcSS) as the pulley ofthe CVT. Two types

41

of plates wifu centre line average rouglmess, R a' of 0.6 and O.lI.llIl were prepared The measurements were repeated at least three times for each sample. In addition to obseIving 1he film formation process, a non-contact optical profilometer meter 'WaS used for post test surfuce characterization. Load

Belt

~~~'

Secondary pulley ¢=' F~

Block shoulder Rocking edge

Clamping force

J

Fig.2 Schematic ofa ball on disk tnbometer

Fig. I Genernl assembly ofametal belt CVT

33 Testoils Automatic transmission fluids (iXfFs) were used for belt CVTs lIDtilfue middle of1heI990s. Specific beh CVT fluids (CV1Fs) were launched wifu fue spread of application to larger vehicles towards fue end of fue 1990s. In fue first stage of1his study, torque capacity level and fiiction characteristics of commercial AIFs and CV1Fs wifu properties as 1isted in Table! were evaluated. For example, AIF 2 has been used as a conventional type AIF for twenty years. AIF I and 3 are applicable to automatic transmissions wifu a slip controlled lock-up clutch system, which have been on a mruket since fue middle offuel990s. CV1FI and 2 are only foruse in CVT and may have higher fiiction coefficient between fue metals. Various kinds of additives such as anti-wear agents, detetgents, dispersants and fiiction modifiers are blended into AIFs and CV1Fs. In fue second stage of 1his study, fue effects of anti-wear additives on tnbological performance lIDder conditions appropriate to fue CVT were investigated. Three kinds of ZnDJPs (primary alky~ secondaIy alkyl and myl group), SP (Sulfur-phosphorus compolUlds) type additive and phosphoric ester as shown in Table2 were used as trial additives. As to ZnDJPs, primuy alky~ secondmy alky and myl group correspond to hexy~ 1-me1hylpentyl and dodecylphenyl respectively The lubricants used were hydro cracked minernl oil based model blends containing each additive wifu fue same phosphorus content of 0.03 mass %. The range of phosphorus concentrations is an average level compared offue commercial lubricants as shown in Tablel. Table2 Composition oftrial additives

Tablel Properties ofconnnercialAIFs and CV1Fs Sample No.

ATF 1

ATF 2

ATF3

Viscosity 40 C

32.2

33.4

36.6

38.1

30.3

mm 2/s

looe

7.48

7.45

7.18

7.20

7.00

Visl.!osity Index

211

200

168

155

Ca

0.07

0.01

0.12

Zn

-

-

-

0.07

-

p

0.03

0.03

0.03

0.06

0.04

N

0.08

0.09

0.19

0.08

0.11

Elements mass 0/.

eVTF 1 eVTF2

Sample No.

Antiwear additives

Base oil

AWl

ZnDTP (Primary .Iky~

204

AW2

ZnDTP (Secondary alkyQ

Hidro cracked mineral

0.05

AWl

ZnDTP (Ary~

viscosity 7 mm'/s(lOO't)

AW4

SP type anl;'wear addilive

AW5

Phosphoricesler

Phosphoros conlent in each sample: 0.03 mass%

42

4 Friction condition between belt block and puHey surface in the cvr 4.1 Calculation of contact pressure and relative tangential slipping velocity between block and puHey in the cvr

In orderto simulate the tnbological characteristia> between the belt and pulley system in the actual CVT using the above mentioned tnborneter, operating conditions such as contacting pressure and relative tangential slipping velocity in this region were calculated. Asstnnptions in this calcu1ation are as follows. 1. Relative tangential slipping between the belt block and the pulley occurs on the pulley with smaller active arc with the belt because there are bigger gaps between neighbouring blocks on a smaller arc pulley, due to the smaller radius of CUIVature, fucilitate slipping. Thus, gross slipping occurs on the primaIy pulley in the case onow speed ratio [7] and the distnbution ofslipping there is constant 2. No tangential slip occurs when the drive torque is at no load. 3. The flat block side and CUIVed pulley surfuce at the pitch in the primaIy pulley is in a line contact, with a pulley radius of CUIVature ofradius R py contact on a flat block side, as shown in Fig.l. 4. The belt assembly and pulley are treated as elastic bodies; therefore, forces on these components due to defonnation are neglected. FigJ shows distnbution of the ring tensions and the block compressive force in the case of the lowest speed ratio and the maximmn drive toyque. If a torque is applied to the drive pulley, a micro slip occurs between the belt and pulley, and consequently the tensions in the straight parts will be no longer equal [11 ]. Here, Tl is the ring tension in the upper side ofthe belt and Tz is that in the lower side ofthe belt In addition to the difference between two tensions (T z- T 1), block compressive force C 1acts in the upper side ofthe belt and it transmits a torque. T 1 and T z are restricted on the condition at the maximum driving toyque. Next, equihbritun of forces acting on the block and on the band in the small active arc area is shown in Fig.4, e being measured from the point at which the belt edges move out of the primaIy pulley. Hence, T is the band tension at angle e, T + dr is the band tension at angle e +de, and C and C + dC are the block compressive force at angle and at angle + de, respectively. Note that the dC in Fig.4 gives a negative value because compressive block forces increase as etends to zero. Considering the equihbritun of forces between components in the active arc area of the primaIy pulley, the following differential equations [11, 12] are obtained.

e

e

(4) )d B = 0

(5)

dC + 2J.l u dF - J.lbdN = 0

(6)

dN - (T -

mb Vb 2

dN -2 J.l a dF -CdB-m V 2 dB =O Ii

a

(7)

a

From equ(4),(5),(6), and (7), with boundary condition when e = 0, T= Tl , C = C 1 T=(T., -mbVb2)exp(J.lbB) + mb V/

(8) (9)

Where C1 and Ti are integral constants, rnaV/ and m bVb2 are centrifugal force per length on the block and on the band, respectively. 11' =l1a / [sin (,1,)+110 cos (A) ] represents the composite friction coefficient

43

between the block and pulley. The constants C] and T] can be determined fium the condition when the belt transmits the maximum torque. The torque capacity M rmx is given by (10) when 0 = a p. T=T 2, C =0, fium equ (8), (9), and (I 0). T], T 2, and C ] will be solved. From equ. (5), (7), and (9), the nonnalload dFbetween the block and the pulley surface is given by. dF

=L {1! exp(.u'B) - C 2f.1a

1 exp(f.1'B)

- rnbVb 2 exp(f.1'B) - rna Va 2 exp(f.1'B)}dB

(11)

Where dO = t I &;, represents the active arc per block, t is block thickness. Here the friction coefficient fl a is obtained from equ.(12)

~= cos (A)

M m• x

(12)

2 x f.1o x Rp

Where Fax is the axial clamping force on the secondruy pulley, M rmx is torque capacity of test oil, R p is the radius to the rocking edge on the primaIy pulley and A is halfthe pulley edge (11°), Fig!. Moreover, the friction coefficient fl b between the block shoulder and imler band is obtained for ring on disk 1nborneter descnbed in our previous work [8]. Forexample,flb withCVTFI is at 0.12. Mean contact pressure Prrmn between the block and the pulley on the primaIy pulley is expressed by P

mean

ffdFxE'

(13)

= 1--16R py xL

Where E' is effective elastic modulus, R py is CUIVa1ure of radius on the pitch in the primaIy pulley shown in Fig. I, L is the block height For example, Prrt:al with a pulley clamping force of25.3 kN is calculated at 105 MPa On the other hand, the relative tangential slipping velocity Va may be calculated from [13] Va =VxSR+100

(14)

Where Vis the belt tangential velocity on the rocking edges, and SR is the slip ratio expressed as equ.(2). Significant gross slip occurs on the primaIy pulley in the case of the maxirmun driving torque at a lower ratio. Here, the value of v;, was used as the slipping velocity on the primaIy pulley. () = 0

e=

(a)Force acting on the band (Primary pulley)

as

Fig.3 Distnbution ofring tensions and block compressive force (1=2.36)

(b)Force acting on the block (Primary pulley)

FigA Equilibrium offorces in the small arc area on the primaIy pulley (I = 2.36)

44

4.2 Lubrication regimes between block and pulley in the CVT Lubrication regimes are a convenient way of classifYing the form of lubrication and fue overall performance in a 1nbological interface, which are determined by four lubrication regimes; hydrodynamic lubrication, boundary lubrication, mixed lubrication and e1astohydrodynamic lubrication. For example, fue Stnbeck diagrnm enables friction to be analyzed throughout fue four lubrication regimes. Here, fue dimensionless parameter representing fue operating conditions is introduced as (15) PmeanRa

I

Where 7J is oil viscosity, PrrtID is the mean contact pressure expressed by equ (13), Va is the relative tangential slipping velocity defined by equ. (14), and R .' = (R .1 2 +R a2 2) 05 represents composite roughness of the two stnfaces. Recent designs for fue belt block side employ grooves wifu centre line roughness of 81lIll in fue traction direction This texture could play a role in removal of oil film between two contacting interfaces and lead to its lubrication being fue boundary lubrication regime. Subsequently, friction coefficient f.1. between fue belt block and pulley in fue CVT were experimentally solved by measurements oftorque capacity, varying fue pulley clamping force and oil temperature. The relation between friction coefficient and 7JV.IPrrem R a' is shown in Fig.5, which represents fue S1nbeck diagrnm for fue belt and pulley system in fue CVT. The friction coefficients in 1his condition were relatively high and aIrnost constant at 0.12. Furfuennore, fue frictional behaviour was essentia1Iy independent of viscosity. Therefore, the contact between the belt block and fue pulley was found to be in fue bOlmdaIy lubrication regime. Next, in order to find fue test condition equivalent to fue CVT, friction characteristics between fue metals were eva1uated by using fue ball on disk 1nborneterunder fue conditions ofnonnaIload fiurn 7 to 33N, oil temperature fiurn 40 to 100°C and plates wifu average surfuce roughness of 0.6 and 0.1 f.lIll.lt is observed 1hat fue friction characteristics in 1his 1nborneter are similar to fuose between fue belt and pulley in fue CVT and fue contact between fue ball and plate lies in boundaIy lubrication regime, as shown in Fig.5. In boundary lubrication fue friction charncteristics are determined by fue properties of surfuce materials and lubricant films at fueir interfaces. These films are called boundaIy lubricant films, which may be classified into 1hree main types of surfuce reaction; physicaIIy adsotbcd layers, chernicaIIy adsorbed layers and films chemicaIIy formed by lubricant additives. Films formed by chemical reaction provide fue greatest film strength and are effective in severe operating conditions such as fue beltlpulley system offue CVI 5 Experimental results and discussion 5.1 Friction characteristics with cornrnercialATFs and CVfFs The friction characteristics wifu commercial XIFs and CVTFs by the 1nOOrneter are shown in Fig.6. Contact resistance in 1his figure represents film strength between fue tnOOlogical interfaces. First, contact resistance in fue case ofbase oil alone was scarcely monitored during fue tests and significant metal contact occurred. This condition could be in boundaIy lubrication because offue little effect of fluid films. CVTFl gave the highest friction coefficient of all tested oils. CVTF2 gave fue second highest friction level and fue friction coefficient of XIFl was lower 1han fuose ofCVTFs. No film formation was observed in fue case of XIF2, resulting in a large wear scar. Contact resistances wi1h XIFl and 3 and CVTF2 were stable at approximately 100 % throughout fue tests. These stabilizations could be caused by chemicaIIy adsorbed films on fue metal resulting fiurn friction modifiers added in fuese samples.

45

The relationship between torque capacity of the CVT and fiiction coefficient in this tnbometer is shown in Fig.7. The torque capacity tests were evaluated at a secondary pulley force of 25.3 kN and primaIy pulley speed of 14m's. The totqtre capacity of ATF2 was normalized as a torque capacity ratio of 1. Standard deviations of both measured torque capacity and fiiction coefficient were within ± 2%. and repeatability fOl.md to be good. Regarding the tendency with CVTF 1 and 2 and AIF 1 and 3 to give good film formation, as the fiiction coefficient was higher, torque capacity in the actual CVT was higher for these fluids. On the other hand, fiiction coefficient of AIF 2 was a little higher than the other AIFs in spite of its lowest torque capacity. One reason forthe difference between torque capacity and fiiction coefficient with ATF2 may be that significant metal contact due to the poor lubricity led to its fiiction coefficient being higher. Actually, wear level withATF2 was larger than other samples, as previously noted. Therefore, it is vital for the improvement of torque capacity by lubricants to give higher fiiction coefficient and to demonstrate good film fonnation on the contact regions. These results also justify the use ofthis tnbometer as a test method to evaluate torque capacity ofthe CVT. 5.2 Friction characteristics with each anti-wear additive Fig.S shows the measurement results of contact resistance with each anti-wear additive at a constant stroke of5rnm, frequency of 22Hz and oil temperature of 1(xtc with varying normal load from 7 to 33N at a rate of IN every three minutes. The data were acquired at a rate of 1000Hz and were rapidly averaged every second The measurements were repeated three times for each sample. First, in the case of sample AWl with primaIy alkyl type ZnDTP, contact resistance stabilized at ahnost 100 % tmder a steady load conditionAW2 of secondmy ZnDlP demonstrated better lubricant film formation thanAWI. On the other hand, film formation by AW3 with myl type ZnDlP was quite different to other ZnDTPs. Contact resistance was ahnost 0"10 at a load less than ISN. After that, lubricant films started to be formed on the contact regions to demonstrate complete films formation above 2SN. Friction coefficient was higher than other ZnDlPs. In general, ZnDlP s generate bolUldary lubricant films by chemical reaction with the surface. This difference in the film formation in connection with ZnDlPs alkyl group could depend on the decomposition temperature ofZnDlPs, which can be defined as the temperature at which the weight loss of each additive reaches 50% by thennal analysis. A lower decomposition temperature means that additive starts to react at a lower temperature or a lower load The decomposition temperature of myl ZnDlP is determined at 345°C which is much higher than that of primaIy type at 235°C. It can therefore be presmned that myl ZnDlP started to react at the temperature on the surfaces equivalent to above the lSN load condition In addition to ZnDlPs, SP (SuIfirr-phosphorus compolUlds) type and phosphoric ester were examined In case of AW4 with SP type in Fig.S, lubricant films were scarcely formed during the tests and gave a 1aIge amolUlt ofwear. Even though suIfirr in SP additive reacted on the contact surfaces, contact resistance from iron sulfide was not observed due to its higher electric conductivity. The effect of phosphorus compolUld in this SP additive seemed to be little on the film formation The film fonnation produced by AW5 with phosphoric ester stabilized more than ZnDlPs. Friction coefficient increased with increasing normal load and the maximmn fiiction during the test was given at a

46

load of 15N. This phosphoric ester has tricresyl alkyl group with a 1arge density of 1.18 g /crrl, which might produce higher traction force between the interfuces than other alkyl groups. 100

0.14

•••

\,

0.12

1:

...""" "...

'.." .. 'E c

.;:

""

0.1

• . -

~

"...

"

~... . ."

0.08 0.06

• Block and pulley in cvr

o

0.04

Triba meter (Ra'=O.6f1m)

A Tribo meter (Ra'=O.1f.U1l)

\,,)

0.02

o

80 60 ....ATFI ~ATF2

40

-6-ATF3 -<>-CVTFI -o-CVTF2

20

-*-8... oil

0

10.7

5xl0'"

10.5

1.5xl0·5

2xl0·5 2.5xl0·5

5

10

15

20

25

Load(N)

Fig.5 Friction chamcteristics between lnbometer ani cvr

Fig.6 Fihn fonnation wilh COJ:IJIrelrial fluids

1.5

~

\,,) 1.4 .S ~ 1.3

~

80

r---------------~+-----_1

"...

.....

.j

€.. 1.2

....

~1.1

;;

"

U

ATF2

~ 1.0 .....

60 40

1-----------:::l=,I>o4----I

20

1-~----+_---------I~~ill1

Poor film formation

0.9

0.09

0.095

0.1

0.105

0.11

Friction coefficient in Tribometer

10

15

20 Load(N)

25

30

35

Fig.7 Relationship between torque capacity ani fiiction ooefIicient Fig.8 Fihn fonnatioo wi1h anti-wear adlitives 53 Instantaneous lubricant film fonnation characteristics Measurements of instantaneous film formation at a step load were carried out by collecting the data at a rate of! OOOHz. Fig.9 (a) shows instantaneous friction cbmacteristics withAWI, primaIy type ZnD1P The moment the applied load was increased fiom 12 to 13N, at 200msec elapsed, the contact resistance instantaneously decreased despite keeping good film formation at a steady load of 12N. The resistance reached the minimmn level at lOOOmsec and the values at ttuning points dropped to 0, which means disappearance of lubricant films at the contact points where slipping tangential velocity is O. The resistance started to be regained at 2000msec and then it grndually increased with the time, finally reaching almost 100010 at 5500msec. It took a slipping distance of605mm (22Hz x 5mmx 5.5sec) for AWl to completely be refonn boundruy lubricant film. Film fonnation fiom AW2 with secondruy ZnDTP is shown in Fig.9 (b). AW2 demonstrated stable film formation the moment the load was instantaneously increased at 200msec. The reason for this difference in instantaneous film formation with ZnDTPs type could be based on the difference in decomposition temperature of each additive. Decomposition temperatures of primaIy and secondruy type are detennined at 235 and 207 °C, respectively. The vcry moment the applied load was increased from 12 to 13N, the contact width estimated by Hetzian elastic equation was widened by 3 IW in diameter. In case

47

ofAWl, prirnruy type ZnDTP seemed not to chemically react on the newly widened contact region at that moment due to higher decomposition tempernture. 5.4 Comparison of friction coefficient with each anti-wear additive As proposed in a previous study [8], higher torque capacity fluids potentially have an advantage in improving overall transmission efficiency tlrrough the reduction of oil pump load. Therefore, the peIfonnance of Belt CVT lubricants should be focused on giving higher torque capacity. The choice of anti-wear additive giving higher friction coefficient between metal-metal interfaces is a key oil fonnuJation technology forthe improvement oftorque capacity. Fig.lO shows a comparison of averaged friction coefficient tlrroughout the tests with each anti-wear additive. The friction coefficient ofAW3, myl type ZnDTPwas the highest ofall tested samples. In addition, the standanl deviation of measured friction coefficients was f01.111d to be within ±O.OO4. The difference in friction between AW3 and other test oils except for AW4 can be regarded as significant There is little difference between AWl, 2, and 5. Fig.ll shows comparison of composite roughness on post test contacting surfaces. The post surface roughness with AW3 was significantly higher than those of other samples. Moreover, the rougher surface tends to produce higher friction coefficient From these results, friction coefficient was related to ll1OIphology of1he worn surface influenced by the lubricants film formation process. There is little difference in friction coefficient under conditions that lubricant films from anti-wear additive are fanned completely. 0.12 I Standard deviation J3N , _ ,. r :_ _ _ _ _ _ 12N __ =4~ _ .~

rrr:TI!IOOnM j t 'Iri M h rI 11 I

~::.

0.11

~ ~

g

~

S

~ ~

.::

~

!

j=:~G]

0.10

I--

i+- --

I

T --

I--

+

TI ..... C""' ___ )

;GD .. u

0.09 AWl

~-

AW2

AW3

AW4

AWS

Fig.! 0 Comparison ofaveraged fiiction coefficient

!:~ ~ wOh.,._",,: ~ ~_ u

-:000

_~oQ

__ 0 0

.~DO

• •00

I

I'

---Ini'ial

_400

"T'"I ...... C ..... _ _ _ )

E

StarlClard deviation

::t.

(a)AWI:ZnDTP(Primary alkyl)

- ~ --- F ------ -- --- -- -=': I--

AWl

(b)AW2:ZnDTP(Sccondary alkyl)

Fig.9 Instantaneous film formation process at step load

--

AW2

I--

--

AW3

AW4

AWS

Fig.!1 Comparison ofroughness on post -surface with anti-wear adlitives

(Strdre=5rrnn, Frequency=22Hz)

48

6 Conclusions Metal-metal tnbological properties on fue transmittable torque capacity of a metal V-belt type continuously variable transmission (B-CVI) were experimentally investigated using a ball on plate type tnbometer (plint 1E77 ), which enables film formation to be monitored during rubbing tests. In addition, fue effects ofanti-wear additive used in B-CVT fluids on bOlmdary film formation were examined.

(I) The contact region between fue belt and pulley in fue CVT was found to be in boundary lubrication regime. It is vital for higher torque capacity to give higher fiction coefficient and to demonstrate good lubricants film formation on the contact regions. The tnbometer used in 1his study was proved to be a relevant test method to evaluate totqUe capacity ofthe CVT. (2) There was a significant difference in instantaneous films formation between primary type and secondary type ZnDTP at the moment of increasing step load. In the case of aryl ZnDTP, 1he films started to fann at a load of ISN and fuen demonstrated complete film fonnation above 2SN. These phenomena were caused by fue variation of decomposition temperattrre of different ZnDTPs. The films produced by phosphoric ester stabilized during rubbing tests more 1han ZnDTPs. (3) Aryl ZnDTP gave higher fiction coefficient 1han ofuer samples. The rougher worn surfuce tends to produce higher fiction Friction coefficient was related to mmphology offue worn surfuce, influenced by fue lubricant film formation process. REFERENCES

1 http:wwwJatco.coJplGANOINAIGAll-l1M 2 Mitsui,H. Trends and requirements of fluids for metal pushing belt type CVTs. Journal of Japanese society oftnbologists,2000,45-6,13-18. 3 Ishikawa,T.,Murakami,Y., Yautibara,R. and Sano,A. The effect of belt drive CVT fluid on fue fiction coefficient between metal components. SAE PapeI972921,1997. 4 Spikes, H.A. History ofZDDP Tnbology letter, 2004,17, 46S. 5 Wada,R and Iwanami,L Xanes study on boundary lubrication films generated from belt-drive CVT fluids. Synopses ofIntemational Tnbology Conference,Kobe,200S,319. 6 Micklem, JD., Longmore,D.K. and Burrows,C.R. Modelling of fue steel pushing V-belt continuously variable 1rnnsmission. Pro.Inst.MechEngrs, 1994,208,13-27. 7 Ide,T. Metal V-belt used for continuously variable transmission for passenger car. Journal of SAE of Japan, 2000,544,4-9. 8 Narita.K., and Priest, M Metal-metal fiction characteristics on fue transmission efficiency of a metal V-Belt type continuously variable transmission. Submitted to Journal ofFngineering Tnbology, 200S 9 Narita,K., Abe,A., Desbimam,J., and Hara,S. Improvement of torque capacity of metal V-belt type CVT fluids. SAE Paper 2003-01-1997,2003. 10 Furey,MJ. Metallic contact and fiction between sliding surfuces. AS.L.E Trans, 1961,4-11. 11 Stolarski, T.A. Tribology in Machine Design BatteJworth-Heinemann, 1990, 128-129. 12 Katsuya, A., Sato,T. and Kurimoto,K. Tnbology in Analysis of behaviour of CVT belt Proceedings ofSAE ofJapan Conference, I989-S, 891,13-18. 13 Takahara, R and Abo, K Heat generation analysis of a metal V-belt for CVTs. Journal ofSAE of Japan, 2000, 54-4,16-20.

49

Design Consideration and Potential of the Milner CVT

(1)

Adrian Hunt(l), Sam Akehurst(l), Stuart Schaat<2) Powertrain & Vehicle Research Centre, Department ofMechanical Engineering, University ofBath (2) Intersyn Technologies, Houston, Texas

ABSTRACT

The Milner continuously variable transmission (MCVT) is a transmission device based on rolling contacts analogous to those found in an angular contact roller bearing. The MCVT is particularly applicable to very high power density applications or those with very demanding packaging constraints, due primarily to the existence of multiple traction contacts on each of the rolling elements. Initial testing of a 12kW MCVT prototype was undertaken in 1999 with promising results [I]. This lead to the further development and testing of a 2nd prototype MCVT rated to 60kW, however the design target of this device was not achieved and performance was hampered by a loss of traction capacity compared to the design specification. The work presented in this paper highlights work undertaken on a full dynamic simulation and analysis of the MCVT and identifies the failure mode of the 2nd MCVT prototype and as a result a redesign of this device has been undertaken such that it can obtain its original design targets. INTRODUCTION

The Milner CVT is shown schematically in Figure 1. Primarily it consists of two outer race halves, two inner race halves, and a number of spherical planets, which drive a carrier assembly via planet followers. The ratio of the MCVT is adjusted by moving the outer race halves axially, a motion achieved in this instance using an outer ballscrew to translate the rotational shift motion into axial separation. The planets move radially to vary the point of contact between the outer races and planets. Conversely if the outer races are moved towards each other the planets are driven radially into the inner race. The inner race assembly is shown in this configuration with a ballscrew assembly that adjusts the clamping load on the planets in proportion to the torque being applied to the input shaft. The ratio of torque applied to normal force is essentially a function of the geometry of the inner ballscrew. Similarly the geometry of the outer ballscrew can be optimised in relation to the traction forces on the outer race such that the shift actuator force can be minimised. MODELLING

A full dynamic simulation of the MCVT has been undertaken in Matlab/ Simulink, highlighted here are some of the main components and features of the model and details of how the model is being expanded to add increased complexity and realism.

51

race ballscrew

Figure 1 MCVT general arrangement

Outer Race

Planet

Inner Race

Figure 2 Race & planet ball arrangement Geometry

The contact geometry at the outer race is effectively a control input to the simulation, as the outer race positions are controlled by the shift actuator mechanism. Simple geometry for the outer race and planet ball contact angles can be used to solve simultaneous equations for the angle () (see, Figure 2). Once () is found further geometry and knowledge of the radii of curvatures of the planet ball and outer race and the radial locus of the outer race can be used to find the radial position of the outer contact patch and the orbiting radius of the planet. This can then be used as an input to a similar analysis with respect to the inner race, where simultaneous equations can again be solved for the contact angle rjJ, see Figure 3., which can be used to calculate the radial position of the inner contact patch, again with a knowledge of component curvature (Rplaneb & Rinner) and the machining locus for the inner races (Rinner,datum). 52

. ,/

I ,h ,(1\"0/11. /.

Rln.danl1ll

/

.. _.4----_.

I

! _______

Rorbi1

I

L_~._.

_____ ._ .

Figure 3 Inner race geometry Additional geometry calculations are performed to evaluate the axial position of the inner races, and the contact angle between the planets and the fixed radius followers on the carrier assembly. The geometry block within the Simulink model passes on these variables to other blocks modelling free bodies, traction conditions and the kinematics_ Later iterations of the model have also included analysis of the deflections occurring in the materials at the contact patches to assess the effect on the overall ratio of the MCVT. Contact Conditions At the points of contact between the races and the planets, contact patches of elliptical profile will be produced. The surfaces of both the race and planet will deform elastically and mating faces will form. The dimensions of these ellipses are governed by the ellipticity ratio, k = b / a (where b and a are the axes of the ellipse), which is principally a function of the geometry of the MCVT for any given ratio. The size of the contact ellipse (while maintaining their ellipticity ratio) is a function of the normal force, or mean pressure between the two components. A more detailed description of the Contact mechanics is described by both Hamrock [2] and Johnson [3]. The dimension of the contact ellipsoids are fundamental to the modelling of the traction conditions at each contact and any losses that occur. Throughout the modelling work described in this paper it is assumed that the pressure distribution within the contact ellipse is purely Hertzian. Force Transfer Each component of the MCVT is modelled as a separate body. In order to calculate relative motions and accelerations of each body a model of the forces acting on them is required. These forces and the states of each body are highlighted below in Table 1:

53

Table 1 Forces and States of MCVT components Forces Input Torque, Ballscrew Torque Normal contact Force Traction Force Ballscrew Torque and Force Inner Traction Force Outer Traction Force Normal Force to Idler Shift torque Normal contact Force Traction Force Ballscrew Torque and Force Load torque Normal Force on Idlers

Component Input shaft Inner races

Planet ball

Outer race

Carrier

States Input shaft speed Inner Race speed Position Relative to input shaft Planet Orbit speed Planet Orbit radius Planet rotational speed Outer Race Rotational Speed Outer Race Positions (Geometry Input) Carrier Rotational speed

In order to calculate the forces essential to the traction modelling, the ballscrew threads need to be modelled. The lead of the thread, given as the product of the thread pitch and the number of starts, directly affects the ratio of axial clamping force generated in the ballscrew to the torque acting on it. Thus, the inner ballscrew geometry can be manipulated to control the expected traction coefficient at the inner and outer race. Additionally, the ballscrew is modelled with an efficiency which changes sense depending on the motion of the inner ballscrew. Typically ballscrew efficiencies are greater than 90% but may reduce under heavy near static loads or at very fine lead lengths. For torque generating axial motion Faxwl hali,crew in .Pitch.Starts Tqbaliscrew.in = ' 2 ' TC1J baliscrew

For axial force generating rotational motion F axial baliscrew in .Pitch.Starts 'hal/screw Tqballscrew,in

=

'

,

2"

Fluid Film Thickness In order to accurately model the traction occurring at the contact ellipses a measure of the entrained oil film thickness is required. The minimum and central fluid film thicknesses are calculated using Hamrock and Dowson's approximation [2]. These approximations have been shown to have relatively high accuracy when compared to full numerical simulations of entrained oil film thickness and are much less computationally intensive, thus enabling the Simulink model to run at a more useful speed. As well as being applied in the main traction force calculations. The other benefit of calculating oil film thickness is as a design guide to identify suitable surface finishes for the rolling surfaces of the MCVT. The lambda ratio (ratio of minimum film thickness to roughness of interacting surfaces), A , is defined as

54

A=

hmin (R2q,a + R2q,b )112

Where hmin is the minimum film thickness and Rq,a and Rq,b are the surface roughnesses of the two rolling surfaces. To ensure pure elastohydrodynamic lubrication A should be a minimum of 3. If the lambda ratio is too low, the lubrication regime can become partial, which will result is asperity contacts occurring through the oil film, which under extreme circumstances could lead to fatigue failure and pitting of the race surfaces. Kinematics and Spin The MCVT rotational speeds can be envisaged as being analogous to an epicyclic gear system with stepped planets, where the dimensions of all the meshes can be varied. Additional complexity exists due to the fact slip (typically up to 3%) may occur at each of the contact points. If we assume no slip takes place between the surfaces, the rotational speeds are easily evaluated. However, within the full dynamic analysis the state (speed) of each component is known and thus the slip speeds can be calculated and applied in the traction modelling routine. The direction of the slip will determine the direction of tractive force generated between the two sliding surfaces. With slip taken into account, the speed which the planets orbit the transmission centre-line (assuming that the outer race is stationary) can be expressed as:

(

R

. OJ

m~" = ~[. R

-u .. - Rplanet,inU'lip,oUI J ".. '

cont,In

R_

R planet,in Reom ,alit + R

j

planet,olll

Since the contact patches are angled relative to the major axes of rotation, by the angles () and ¢ there will be a spin motion within the contact patch as described in Figure 4. This is best understood by considering the transmission in two states I. if the planet was orbiting about the bottom of the inner race, such that t/FO, then the motion would be pure rolling and no spin would exist. 2. If the planet was running high up the inner race, such that t/F90°, then the motion of planet rotation would be pure spin Similar scenarios exist at the outer race contact. Therefore for intermediate angles of () and ¢ there will be a component of spin acting through the contact patch. The spin velocity can be applied to the EHL traction model, with high spin velocities producing excessive losses in the traction contact and reducing the tractive force generated for a given sliding speed.

55

i

._._.~_._.J_ Figure 4 Spin kinematics

.. . . .

. ..........:, .

~~-

~'''-''--------~_ ....t''.:''_''''''''

.

. . ............--------------..--..--....--..: ... '/~- ..../------~-~;--.,--...,....: . ,../

---------.

...../ _........

. -.,;. '-.. ,........ ~,

... ////_--------~- •. - ........... -.... >. .. ,,'...:

,:~;;~ ~::::=====::::-::~ . :-: ~:. :~ ';;~: ,'" - -- -~-'". '." ',,','

---

.

,/",,: ::==== ==:,,:.~': ",\' ....

///,

,,'

\'

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x 10-3

Figure 5 Resolved Velocity vectors in a contact patch for slip in the x direction and clockwise spin

y position(m)

-I

-5

x position(m)

Figure 6 Hertzian pressure distribution (Pmax = 4.2 GPa) 56

Traction Modelling

The traction model is complex and is therefore described in brevity in this paper. It is coded as a Matlab function imbedded in the Simulink model which is called at every time step for both the inner and outer contact patches. The inputs to the traction model are: 1. contact dimension of the ellipse 2. mean speed (entrainment speed) into the contact patch 3. Spin speed 4. Slip speed (in traction plane) 5. Side slip speed (perpendicular to traction) 6. Film thickness 7. Contact pressure 8. Lubricant properties (base viscosity, pressure viscosity coefficient and bulk lubricant temperature) The outputs form the traction model are: 1. Traction force 2. Side slip force 3. Torque loss due to spin in the contact Within the traction model the contact ellipse is segmented and a piece wise evaluation of local velocity (from spin, side slip and slip), see Figure 5, along with the Hertzian pressure, Figure 6, is performed. The shear stress is then calculated (including a limiting shear stress model based on temperature and pressure). Two methods of dividing up the contact patch were considered; producing elliptical areas originating from the centre of the contact patch, or a Cartesian grid with equally divided areas. The latter was adopted in order to retain small sub-sections towards the edge of the ellipse, where the spin could be significant. Finally the local shear stresses and their respective vectors are integrated over the contact ellipse to find the output variables defined above. SIMULATION RESULTS & DISCUSSION

The models highlighted above indicate the capabilities of the model that has been developed. However within the context of this paper we shall be examining some simple comparisons between the YP2 12kW test transmission which under went successful testing and the YP3 60kW device which did not meet its original design targets, to try and identify any issues with the YP3 deign and to identify remedial actions that may be taken to optimise the YP3 design. As the traction contact conditions vary significantly between high and low ratios, the simulation conditions were set to run over a full ratio sweep. In order to consider a worst-case scenario, the input conditions were at the rated torques and relatively high operating speeds. In the following plots traces are shown for the 12kW YP2 prototype running at 400rad/s and 30Nm, the 60kW YP3 device running at 400rad/s and 75Nm (30kW), and the YP3 operating at 400rad/s and 150Nm (60kW). The plots show the transmission shift from low ratio to high ratio and back to low ratio where Ratio =

(Oin

mOUI

The specifications of the YP2 and YP3 transmissions are detailed in Table 2 below:

57

Table 2 Specifications of YP2 and YP3 MCVTs YP2 Dia 100 30 12 5:1 3 17.5; 17.5 25.4

Package size (mm) Input torque rating (Nm) Power Rating (kW) Ratio range Number of Planets Race curvature (mm) Planet Ball diameter (mm)

YP3 Dia 150 150

60 5:1 4 20.7; 24.5 34.925

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i 0.055 I0.050~----:2:---------:4'------:6-----=8--1;;;0--:1;';;2--C1'o-4---;16;;-------7. 18;;-----:!20 Time(s)

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Time(s)

Figure 8 Traction coefficient required at outer race over ratio sweep Traction Figure 7 and Figure 8 show the required traction coefficient at the inner and outer race contacts respectively. The traction coefficient is a good indicator of the device's ability to maintain traction between the races and planet. Typically the MCVT is developed

58

for a traction coefficient of 0.07 or less as this gives a good safety margin to ensure that traction is maintained even under vigorous transient events. It should be noted that the traction coefficient is a relatively simple approximation that does not take into account the fluid properties, and the operating conditions of the rolling contacts. Therefore devices operating at high temperature (reduced viscosity) or with high levels of spin tend to require a more conservative safety margin for the chosen traction coefficient. Some assessment of these effects is made within the Simulink model with the full traction model implemented. Figure 7 & 8 clearly shows that the required traction coefficient in the YP3 deign is much higher than that of the YP2 design. Additionally the traction coefficient can be seen to increase as the transmission shifts to a higher ratio, this mechanism is due primarily to the contact patch moving closer the centre line of the MCVT. Therefore for a given input torque a higher traction force must be supported. The curved profile is due to the change in contact angles () and ¢ during the ratio change. While the difference in traction force at the outer race for (green and blue lines) is due to the reaction force from the idler follower on to the planet which acts at varying angles during the ratio change. It is clear from the traction force analysis that the design of the YP3 is very marginal with respect to supporting traction without gross slip throughout the operating range and during transients. It is therefore considered that this was the failure mode of the YP3 design. Sub Surface Material stresses The material stresses are modelled based on Lundberg Palmgren analysis [2 & 4], which additionally allows the depth of maximum stress in the contact patch to be evaluated. Figure 9 and Figure 10 show the maximum subsurface stress levels at both the inner and outer race respectively, in both cases these stresses are always less than 2GPa, increasing with respect to torque on the input shaft. The magnitude of the stresses seen in the YP3 design is very similar to those seen in the YP2. Results also show that the depth of the maximum stress increases in the YP3 design, thus requiring surface hardening treatments to be undertaken to a greater depth in order to maintain reliability. The material stresses relate to the sub-surface stresses caused by material deformation over the contact patch. If these stresses are too large, cyclic loading can cause fatigue failure. It is interesting to compare these stress levels with those published for other CVT types [5], which are a very similar magnitude and indicate that a highly viable fatigue life for the MCVT is achievable. Film thickness Figure 11 and 12 show the central film thickness in the inner and outer contact patches respectively. The film thickness in the YP3 MCVT shows a larger variation than that of the YP2, this is due to the larger dimension of the YP3 design resulting in larger operating radii. This in turn gives larger entrainment speeds into the contact patches encouraging oil film thickness propagation. Both the YP2 and YP3 film thicknesses are within sensible ranges to support EHL traction with good ground bearing surfaces on the mating components.

59

1.9

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Figure 9 Inner race material stresses over ratio sweep 1.6 x10• _.... VP2 Design 12kW - . VP3 Design 30kW - . VP3 Design 60kW

1.4

r -I

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Figure 10 Outer race Material stress during a ratio sweep

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Figure 11 Inner race lubricant film thickness

60

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Figure 12 Outer race lubricant film thickness Inner ball screw geometry It is clear from the modelling description and from the results analysis presented here

that the inner ball screw geometry directly controls the ratio of contact normal force to traction force on the planet ball. In order to optimise the forces contained, e.g. minimising the maximum contact pressure or reducing the traction coefficient required at the races, comparisons can be carried out between thread geometries. In particular the YP3 design was re analysed to assess a more suitable inner ballscrew geometry that would enable the device to perform to its design specification. By reducing the pitch, but keeping the same number of starts, the required traction coefficient could be reduced to a level achieved in the YP2 model. As a downside to the reduction in lead length, the pressure on the contact patch increases as do the dimensions of the contact patch. Both these factors could be detrimental to the performance of the YP3 due to a reduction in potential fatigue life (if the cyclic stresses are too great) and additionally due to increased losses due to greater spin torque within the contacts. However, by careful optimisation of the race way geometries and robust selection of inner ballscrew pitch a good trade off between requirements can be achieved. Figure 13 and Figure 14 show the effect of reducing the ballscrew lead for the YP3 MCVT on the required traction coefficient at the inner and outer race respectively. The black line indicates the performance of the YP2 design. It can be seen that reducing the lead to IOmm reduces the demand traction coefficient at both ratios to a similar levels seen by the YP2 design. Figure 15 and Figure 16 show the comparable mean pressure plots for the inner and outer contact patches. It can be seen in Figure 16 that the contact pressures at the outer race are comparable to those seen in the YP2 design and the corresponding sub-surface stresses are of a sensible magnitude. However, at the inner race the reduction in ballscrew lead shows an increase in the contact pressure, but by a smaller percentage than the reduction in traction coefficient. While the reduction in ballscrew lead will enable the YP3 to achieve its design targets it may become necessary to reduce the torque specification of the device by a margin to guarantee a suitable fatigue life, although sub surface stress levels are still a sensible magnitude.

61

O·08 i-,.-----,.-----,.-----,.-----,.-----r;==::=.=:====::::::====iI

-YP2 Design 12kW ····YP3 Design 12mm Lead . YP3 Design 11 mm Lead ~ .. YP3 Design 10mm Lead

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-

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Figure 13 Effect of varying lead length on inner race required traction coefficient

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Figure 14 Effect of varying lead length on outer race required traction coefficient -

YP2 Design 12kW .. YP3 Design 12mm Lead

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62

VP2 Design 12kW -----YP3 Design 12mm Lead ~ -YP3 Design 11 mm Lead - - -VP3 Design 10mm Lead

2

4

6

8

10 Time(s}

12

14

16

18

20

Figure 16 Effect of varying lead length on mean outer race contact pressure CONCLUSIONS & FUTURE WORK

The modelling work described in this paper has lead to the development of a Simulink model that can be readily used as a design tool to optimise the sizing of future MCVTs and their respective geometries to best meet desired operating targets. The model also has predictive capability for examining efficiency and fatigue life characteristics of a range of MCVT configurations. Initial analysis of an earlier MCVT design has identified a marginality in the designed traction coefficient and a new design has been proposed with a revised inner ballscrew geometry to rectify this issue. This modelling capability has been augmented by ongoing design, manufacturing, and testing over the past 24 months on existing and new designs. Additionally a new transmission test facility has just been bought on line to allow more detailed performance analysis of the MCVT. This facility will be used to perform a rigorous validation of this phase of the modelling and latter phases. New components are being manufactured for the YP3 design with the new configuration ballscrew and the YP3 will then under go further testing to ascertain its performance and again provide validation data for the developed models. Additionally a range of other developments to the MCVT are being undertaken and investigated, these include: A compact MCVT design in an IVT configuration for application in a steerable drill head for down hole oil exploration (currently at functioning prototype stage); Development of high torque MCVT using non-spherical planets [6], modelling phase complete; Application of hydraulic ratio control to the MCVT for improved clamp load optimisation and ratio control. Due to the high power density and compact design of the MCVT the following potential application areas are being considered: Variable speed front end ancillary drive (FEAD); Variable speed Supercharger drive; Automotive transmissions [6]; Power take off applications; Machine tools; Motorcycle, quad bikes and snow mobile transmissions ACKNOWLEDGEMENTS

The authors would like to thank Intersyn Technologies for the funding to carry out this research and allowing this work to be published.

63

REFERENCES 1. Akehurst, S, Brace, C. J., Vaughan, N.D., Milner, P. And Hosoi, Y., 2001 Performance Investigations of a Novel Rolling Traction CVT, SAE Paper No. 2001-01-0874 also in Transactions of the SAE, Journal of Vehicle Design 2002 2. Hamrock, B. J., Fundamentals of Fluid Film Lubrication, ISBN 0-07-113356-9, McGraw-Hill 3. Johnson, K. L., Contact Mechanics. Cambridge: Cambridge University Press, ISBN-I0: 0521347963 4. Lundberg G. Palmgren A. Dynamic capacity of Rolling bearings, Acta Polytech., Mech. Eng. Sci., vol. 1, no. 3 pp.6-9 5. Lee A.P, Newall J., Goto M., Misada Y., Ono Y., Experimental Validation of Full Toroidal Fatigue Life, 04CVT-21, 2004 International Continuously Variable and Hybrid Transmission Congress, September 23 rd - 25 th , 2004, UC Davis 6. Milner PJ. Milner CVT for Hi~h Torque Applications, presented at VDI CVT 2002 Congress, October 7th/8 t , Munich, VDI book No. 1709, ISBN 3-18091709-1

64

Optimum Engine Models for Diesel Automotive Powertrain Development Processes R.P. Osborne and N. Weaver Ford Motor Company, Dunton, UK The intent of this investigation was to recommend a single engine modelling configuration to support all typical powertrain development tasks. Three model categories were defined namely: engine cycle simulation, mean value and non-physical. The options were critiqued by their ability to predict the relevant powertrain attributes of turbocharged high-speed direct injection diesel engines in passenger car applications. A mean value model supplemented by neural networks was proposed. Predictive accuracy was established by directly comparing the proposed model output with that of cycle simulation in a total vehicle simulation application. 1 INTRODUCTION With the increasing demand for computational prediction of engine in-vehicle behaviour and interaction, the need for an accurate but computationally efficient engine model has never been greater (1, 2). The ideal model being accurate enough to predict the appropriate attributes but having run-time efficiency to allow tasks such as desk-top calibration of electronic powertrain control units, hardware in the loop (HIL) controller simulations and transient vehicle performance and emission predictions. For the purposes of this study three distinct categories of engine model were defined broadly differentiated by their physical content: • Engine cycle simulation codes (3): These capture the thermodynamic, fluid-flow, heat-transfer, combustion and pollutant-formation phenomena that dictate the performance of engines. They offer crank angle based resolution of all simulated quantities. • Mean value models (4, 5): These resolve cycle averaged quantities, and are nonlinear. Their formulation contains the minimum amount of differential equations to ensure the engine variables of interest are captured. • Non-physical or 'black-box' models (6): These are typically numerical correlations to empirical data and are often based on steady state data (7). Is it recognised that in some practical applications this categorisation may not be clear-cut. In addition, black-box models can be formulated to simulate transient conditions (6) but require a great deal of test resource. Figure 1 shows the three categories of model in ascending order of physical content. Clearly, the dependence on test data reduces with increasing ability to simulate the physical processes. This is indicated graphically by the thickness of the arrows. Their relative capability against the key attributes (performance, fuel economy and drive cycle emissions) is designated. 2 ENGINE MODEL SELECTION The upper section of Figure 2 lists the model applications that are carried out in a typical diesel powertrain development process. For each model application the fundamental requirements are denoted by 'R'. In the lower section the three model categories are ranked against these same requirements.

67

IF~I~Ono~1 ~ CycleSiImIanon

I

- -

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Q- ---- ~

I I I MeanVal~

Models

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--~----~----Q I I I

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--G:J-----~----QJ

ExceJent capability ,/ ,/ ,/ ,/ ,/ S01m capability ,/ Good capability

Figure 1 Model Categories versus High Level Powertrain Attributes The main conclusion is that cycle simulation is the best candidate for a single engine model for all considered applications. Its only drawback being lack of real time capability, which is a fundamental requirement for hardware-in-the-Ioop. Clearly, fast execution ability is desirable for all applications. Non-physical models have the best potential for rapid evaluation. In this application they have limited ability to capture the transient operation of a turbocharged diesel engine under many driving conditions. A shortcoming of mean value models is their inferior predictive accuracy compared to cycle simulation. An ideal would therefore be a mean value model enhanced where necessary to deliver cycle simulation levels of accuracy.

Real Time

Crank Angle Resolution

Transient Torque

2 Drive Cycle

Thennal (Engine Structure)

R

R

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R

R

R

R

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4 Engine Control Design 5 NVH

R

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6 Powertrain Matching 7 Durability

States of working fluids

Model Requirement

Model Application I Desk-Top Engine / Vehicle Calibration 3 Harware-In-The-Loop

Steady State Torque

R R

--_.

8 Emissions

R

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Fundamental Requirement R

Model Capability

Model Types I Cycle Simulation 2 Mean Value 3 Non·Physical

~~~

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Excellent capability ././" Good capability ~~ Some capability v'

Figure 2 Model Applications and Capability versus Requirements

68

3 PROPOSED OPTIMUM MODEL DESCRIPTION

The requirements defined in section 2 dictate real-time capability and therefore limit the physical content to the mean value level. It was therefore necessary to assess the predictive limits of mean value assumptions and implement methods to correct any inherent inaccuracies. Using cycle simulation as the desired level of accuracy, the key differences between typical cycle simulation and mean value formulations are highlighted. The actual steps taken to enhance the mean value model using neural networks are then described. 3.1 Mean value model description

The high level structure of the mean value model used as the basis for this study is shown in figure 3. The intake and exhaust system is divided into a series of volumes in which mass and energy conservation, supplemented by ideal gas relationships is used to provide gas temperature, pressure and composition during simulation. The state of the gas contained in the volume is considered to be identical throughout. Also velocity effects are ignored. In this example, the engine intake and exhaust is split into three volumes (intercooler, intake manifold and exhaust manifold). Mass fluxes, gas states and composition of flow between the respective volumes are defined using various submodels: • Compressor: Provides mass flow and outlet temperature versus turbo charger rotational speed, pressure ratio (outlet to inlet), inlet temperature. • Cylinders: Mass flux from intake to exhaust is calculated based on intake manifold volume, temperature and pressure, exhaust manifold pressure and fuel flow. Exhaust gas temperature is obtained from a relationship based on inlet manifold gas state and fuel flow. • Turbine: Provides mass flow and outlet temperature versus turbocharger rotational speed, pressure ratio (inlet to outlet), inlet temperature, and inlet gas ratio of specific heats. • Intercooler (air to air): Provides intercooler outlet temperature based on charge air mass flow, inlet temperature and coolant air temperature. • Orifice: Standard orifice assumption (3). Provides mass flow dependant on inlet and outlet pressure, inlet temperature and inlet gas ratio of specific heats. In this example, the orifice sub model is used to simulate overall intake system pressure drop, EGR valve flow and exhaust system back pressure. Further sub-models are included to form a complete model of the engine: • Air Filter: Provides inlet depression from ambient atmosphere based on instantaneous mass flow through the compressor. • Turbo Charger Mechanical Inertia: The instantaneous turbocharger rotational speed is calculated using a mechanical power balance between turbine and compressor assumed to act on the rotational inertia of the rotors and shaft. 3.2 Mean value versus cycle simulation

This subsection describes the key differences between mean value and cycle simulation models that potentially drive differences in predictive accuracy. The discussion is partitioned by relevant sub model.

69

Intercooler I1T

Air Filter

Cylinders

v,

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®

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...

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= f(Pin,PoupT;n,r)

Vo

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NNo

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(p,T,r/J) = f(time)

Figure 3 Enhanced Mean Value Engine Model Schematic 3.2.1 Intake and exhaust system volumes Cycle simulation models provide intake and exhaust system gas states with crank angle based resolution whereas mean value models provide cycle averaged quantities. This does not present a problem in this application as current technology engine control systems only require cycle averaged charge air related quantities (e.g. inlet mass air flow, inlet manifold temperatures and pressures). Provided the overall cycle averaged mass and energy fluxes are accurately captured, mean value assumptions are adequate. 3.2.2 Cylinders Cycle simulation models include multiple cylinders and resolve gas states with crank angle levels of resolution. In addition they capture gas dynamic behaviour which affect the instantaneous mass fluxes through the intake and exhaust valves and hence the overall volumetric efficiencies of the individual cylinders. Mean value models typically have no physical representation of the engine cylinder and rely on empirically obtained data to provide cycle-by-cycle cylinder averaged volumetric efficiency, and exhaust gas temperature. This data is normally entered into the model by either mappings in the form of linear interpolation tables or equations based on regression analyses. It is the authors' experience that cylinder models based on steady state test data can provide inadequate results. This is driven by the inability on steady state tests to force the turbocharger to run at conditions experienced during transient operation. Because of crank angle resolved cylinder pressure, cycle simulation codes are able to predict cyclic torque at the engine crankshaft. As for the cylinder flow and thermodynamic quantities, mean value model formulations typically resort to test data to obtain a mapping of cycle-averaged torque. For most applications cycle averaged torque is considered of adequate accuracy (with the exception of driveline resonance 70

and NVH studies). However steady state obtained torque may be inaccurate during transient operation of a turbocharged engine due to differing pumping losses experienced by the engine. 3.2.3 Compressor Cycle simulation and mean value models typically share the same basic numerical formulations to capture the compressor characteristics i.e. use of manufacturers steady state mapping data obtained from testing on a flow rig. The mapping data is usually entered in the form of discrete operating points that are extrapolated where necessary to areas of operation not covered. The data are utilised in the simulations by linear interpolation between the mapped points at each simulation time step. As in the real engine, the cycle simulation code will present the compressor model with pulsing flow driven by the individual intake events of each cylinder. The compressor model will respond accordingly to this pulsing flow giving a close approximation of the actual crank angle resolved mass flow and compressor efficiency. The mean value model with cycle averaged intake conditions is therefore incapable of accurately capturing the true cycle averaged compressor mass flow and efficiency. 3.2.4 Turbine The same observations for the compressor apply for the turbine. However, the pulsing flow at the turbine entry is considerably larger in magnitude with high temperature as well as pressure gradients. The mean value assumption clearly ignores any 'pulse charging' (8) effects and depending on the operating point can provide gross predictive errors for turbine efficiency.

3.3 Proposed solution for enhanced mean value model Increasing the physical complexity of the mean value model was discounted as the realtime capability had to be preserved. In pursuit of the desired goal it was proposed that the weaknesses inherent in the mean value assumptions could be corrected using data generated by the cycle simulation code. This was achieved by the following procedure: 1) Obtain data from the cycle simulation code using a formal design of experiment (DOE) technique. 2) Use this data to train neural networks to supplement the existing turbocharger and cylinder models. 3.3.1 Design of experiment (DOE) A commercial DOE and optimisation tool was set up to allow input data to the cycle simulation code to be changed automatically for each experimental design. Although the data was based on fully converged steady state simulations, turbo charger speed was allowed freedom in the experiment. This was an attempt to capture the turbo charger characteristics over a wide enough range to allow effective simulation of transient behaviour. The DOE used for results presented in this paper was a modified Latin Hypercube type and contained two thousand individual experiments. 3.3.2 Neural networks The full DOE data set was grouped in appropriate forms for training of the neural networks. Figure 3 shows where the neural nets were applied to their particular submodels (NNI to NN3). The data analysis and neural net training and generation were carried out using a commercial tool. The neural nets used for this study were of the

71

conventional feed-forward type with two hidden layers. The number of neurons per layer was increased for each application until the desired accuracy was obtained. Route mean squared error of neural net output versus DOE result greater than 0.99 was used as a metric for adequate accuracy. 3.3.3 Engine model test set up Figure 4 provides an overview of the total vehicle system model used as a test harness to allow comparison between cycle simulation and enhanced mean value models. The vehicle system includes a powertrain control module simulation, a six mass representation of the transmission, driveline and vehicle, and a 'robot driver' model to allow simulation of prescribed drive cycles. In this example, the robot driver was provided with data defining the first 200 seconds of the New European Drive Cycle (NEDC). The total vehicle system model architecture was configured to allow the engine model (either cycle simulation or enhanced mean value) to be interchanged.

4 RESULTS Figures 5(a) and 5(b) show a series of output data from the cycle simulation and enhanced mean value models. All plots are time aligned and represent the first two hundred seconds of the NEDC. In general the agreement between cycle simulation and enhanced mean value models is excellent for all quantities. The results for gas temperatures of the exhaust and inlet manifolds show the largest discrepancies. Table 1 contains total summed quantities for the two hundred second NEDC run. For total air mass and total brake energy good agreement is shown. However, for total fuel mass there is a -7.7% difference. Vehicle Sensor Signals

Electronic Powertrain Control Module Simulation

+-

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PCM Actuator Models

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r------, 1 Test Engine

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Figure 4 Total vehicle model schematic

72

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5 DISCUSSION

One of the key assumptions in this work is that cycle simulation engine models have a level of predictive accuracy to provide a reference solution to judge other engine models with lower physical content. Previous work (9, 10) documents cycle simulation used for various aspects of HSDI diesel calibration where excellent agreement between prediction and measurements taken from real vehicle testing is shown. An area where cycle simulation has only partial capability is in detailed aspects of in-cylinder combustion and emissions. One possibility is to use similar methods as proposed in this paper (and others (11» to supplement the shortcomings of cycle simulation with either test data or output from higher level physical approaches such as 3D CFD. An absolute requirement for the enhanced mean value model developed for this study was to achieve real time execution ability. Measurements made on a standard desk top PC equipped with a three GHz processor confirmed 0.7 times real time for the total vehicle simulation analysis. This compares to approximately 30 times real time for the same simulations running with cycle simulation. Use of a total vehicle model as a test harness for comparing different engine model topologies gives a direct indication of a model's ability to interact with the rest of the vehicle system. The time aligned results for brake torque and engine rotational speed in figure 5(a) support the conclusion that from a vehicle perspective, the cycle simulation and enhanced mean value models are directly interchangeable with insignificant differences in overall result. Close agreement for the exhaust and intake manifold pressures and turbocharger speed is strong evidence that the enhanced mean value model adequately captures the mass flux from intake to exhaust, and exhaust manifold gas state. Clearly, the true cycle averaged turbocharger mass flows and efficiencies are well defined by the neural networks employed. The discrepancies in the inlet manifold temperature results in figure 5(a) are directly attributable to differences in the intercooler models employed in the cycle simulation and mean value cases. In absolute terms these differences do not generate significant errors in overall charge air mass flow as supported by the MAF sensor results given in figure 5(b). The marked differences in exhaust manifold gas temperature in figure 5(b) require more detailed explanation. Further investigation has shown that the dominant cause is directly due to the differences in heat transfer assumptions made between the enhanced mean value and the cycle simulation models. The neural network employed to provide exhaust manifold gas temperature in the mean value model was trained from fully converged steady state cycle simulation results. In addition, the enhanced mean value model contained no mechanism for heat transfer to the engine structure. It was therefore unable to capture 'transient' heat transfer phenomenon due to the thermal inertias of the engine components. This omission was directly linked to the differences in exhaust gas temperature prediction between the models. Methods to apply a transient heat transfer characterisation in the enhanced mean value model will be a subject for future work. On initial inspection the -7.7% discrepancy on overall fuel consumption (see table 1) is cause for some concern. The total brake energy supplied to the vehicle was almost identical between the cycle simulation and enhanced mean value models (-0.7% difference). The enhanced mean value model was therefore under predicting brake specific fuel consumption. Further examination of the total data set showed that this was due to inaccuracies in the neural network supplying cycle averaged brake torque

75

output, particularly at lower loads. Future corrective work will investigate refonnulation of the brake torque neural network and training from a larger data set. 6 CONCLUSION Cycle simulation offers the most accurate solution for engine modelling in powertrain development processes. It is also capable of accurate prediction of many of the relevant quantities with minimal reliance on test data. In practical applications mean value models represent the maximum allowable physical complexity for real time analyses. The accuracy of mean value models can be improved by using neural networks to supplement the inherent limitations of key sub models. Cycle simulation model output provides an excellent data source for neural net training. A single engine-modelling configuration to support all typical powertrain development tasks has been proposed. The close agreement that has been shown versus cycle simulation confinns that this methodology should fonn a basis for future development. REFERENCE LIST

1 C Krug, J Liebl, F Munk, A Kammer, H-C Reuss, 'Physical Modelling and Use of Modem System Identification for Real-Time Simulation of Spark Ignition Engines in all Phases of Engine Development', SAE 2004-01-0421,2004. 2 A Kammer, J Liebl, C Krug, F Munk, H-C Reuss, 'Real-Time Engine Models', SAE 2003-01-1050,2003. 3 J B Heywood, Internal Combustion Engine Fundamentals, McGraw-Hill, 1988. 4 E Hendericks, 'The Analysis of Mean Value Engine Models', SAE 890563, 1989. 5 E Hendericks, 'Mean Value Modelling of Large Turbocharged Two-Stroke Diesel Engines', SAE 890564, 1989. 6 0 Grondin, R Stobart, H Chafouk, J Maquet, 'Modelling the Compression Ignition Engine for Control: Review and Future Trends', SAE 2004-01-0423,2004. 7 R J Lygoe, 'Fitting Automotive Microprocessor Control Look-Up tables to a Response Surface Model using Optimisation Methods', SAE 981459, 1998. 8 N Watson, M S Janota, Turbocharging the Internal Combustion Engine, Macmillan, 1982. 9 R P Osborne, 'Concurrent WA VE/MATLAB Simulink Simulation Applied to HSDI Diesel ECU Calibration', Fourth Ricardo Software International User Conference, Detroit, 1999. 10 R P Osborne, 'Total Vehicle System CAE Modelling Applied to Electronically Controlled Diesel ECU Calibration', I.Mech.E. conf Computational and Experimental Methods in Reciprocating Engines, 2000. 11 I Brahma, Y He, C J Rutland, 'Improvement of Neural Network Accuracy for Engine Simulations', SAE 2003-01-3227,2003.

© Ford Motor Company Ltd.

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Hardware-In-The-Loop simulation (HIL) for production engine development Carsten Haukap, Dr. Karsten Ropke, Berthold Barzantny IA V GmbH - Ingenieurgesellschafi Auto und Verkehr, 10587 Berlin

ABSTRACT

The demands engine developers face today are increasingly complex. Improved driving comfort in conjunction with stricter legal emissions laws and shorter development times require new innovative technologies and forward-looking methods need to be developed and established. In this regard, the Hardware-In-the-Loop simulation (HiL simulation) at the test bench can make an important contribution to decrease the development times. The following gives an overview on the goals of the HiL simulation as well as the configuration of the HiL test bench at lAY. Furthermore, the used physical models are introduced. Finally, some first measurements of the simulation at the test bench are shown.

INTRODUCTION

Stricter legal emission limits and increasing customer expectations lead to a growing number of calibration parameters and thus to a higher engine control complexity. This development can be observed in the field of Otto Diesel engines. At the same time, the development time from the first idea up to the introduction of a new engine in series has become even shorter, while the costs have to be reduced. To meet the requirements of this trend, the method "Design of Experiments" (DoE) is widely accepted as a suitable tool in the automotive sector and its supplying industry [2, 6]. Likewise, this method is broadly applied in the IAV during the advanced development stage up to the series applications. Therefore, an extensive knowledge of the practical every day usage of DoE exists, after this method has successfully passed the test and experimental phase. However, the classical DoE methodology is restricted to static applications, so that its main application field is the basic calibration of Otto and Diesel engines. The basic procedure - namely the parameterization of an empirical or physical model of the process using a minimum of measurements - can be applied to other engine calibration fields. Thus, empirical models from signal processing (e.g. ARMA models) can be used to model the dynamic behavior of engines. The parameterization of the ECU is carried out on the basis of these models. Whereas in the past the engine test bench was mainly used for static calibrations, actually, standardized test cycles are carried out in the test cells. The trend of a further adaptation of dynamic functions at the engine test bench is apparent. In this context, the HiL test bench can be applied. Therewith, all vehicle components are simulated that do not exist as genuine parts, while the engine runs on the highly dynamic test bench [2]. In this way, many investigations can be carried out, which, up to now, have only been possible with an entire vehicle.

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MODEL BASED CALIBRATION APPROACH The process of the model-based calibration is illustrated in Figure I. First, the task definition is carried out. In this step, the model input and output values have to be determined on the basis of a specific calibration task. Also, the type of the model must be defined: static or dynamic, empirical or (partly) physical (etc.). During the phase of experimental designing! -preparation the test plan is created, which is optimal with the chosen model type. For this step, the MBC-Toolbox of The Mathworks® and in-house tools are applied. For all measurements at the test bench, the IA V uses the in-house development MPe, which allows a flexible and task-oriented test bench automation. MPe (MatlabPriifstands-IAV-Interface) consists of several modules, from which the task-specific program is generated.

on the test bench

Definition of factors Experimental design

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Figure 1 IA V Z-Process for the model-based calibration The next step is the model fitting process. Depending on the chosen model type either polynomials, neural networks or physical models are fitted to the existing data, so that the model error is minimized [6, 7]. Beside the ability to predict the fit data, the models need to have a generalization capability, i.e. robust predictions must be possible in areas, where no measurements have been made. In Figure 2 several model types are illustrated. On the horizontal axis the physical relevance of the models is depicted, whereas the vertical axis shows the complexity. Pure physical models have a very complex structure, but also show a high physical relevance. For such a model type a lot of detail knowledge is necessary, to apply them for specific calibration tasks. Although pure data-driven models (also called Black-box models) can easily be adapted to specific data, they model measurement errors in addition to the meaningful information. To obtain models, which are suitable for the calibration process, two possibilities can be used. On the one hand, physical models can be simplified in a manner that only a few engine parameters have to be known. In this case, the data fitting process is straightforward. For this purpose, often empirical coefficients are applied, which are 78

used to fit the models to specific data sets. On the other hand, the creation of data-driven models is possible, in which physical knowledge (e.g. the order of the polynomials and the type of interactions between the input factors) is integrated. Both model types show a moderate complexity and can be interpreted physically. Furthermore, data-driven models have the advantage that no engine parameters have to be known. Complexity

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Figure 2 Model complexity vs physical relevance for different type of models. The next step in the Z-Process is the optimization and ECU (Engine Control Unit) table filling. The goal of the engine calibration is to obtain ECU tables which fulfill several criteria. Thus, on the one hand the fuel consumption has to be as small as possible, while on the other hand the emissions should meet the legal requirements. Furthermore, the driving comfort should be brand-specific. The demand on a good drivability leads to the necessity of smooth ECU maps. The degree of smoothness is usually determined during the calibration process. Furthermore, the optimization is often multi-dimensional and thus includes several target criteria, whose values are not a priori known. Hence, a Pareto optimization is applied. In this approach, the optimal status is achieved when no target value can be improved without deteriorating another one. Depending on the definition of the overall optimization target criterion different Pareto-optimal solutions are found. Consequently, the optimization is an iterative process, since the best Pareto solution is to be retrieved. This is particular important for Diesel emission optimizations. Classical gradient-based optimization approaches usually cannot be used for such complicated problems. Modern approaches such as the evolution algorithms or "simulated annealing" approaches are a good alternative. To achieve the best possible efficiency, a process automation is desirable. However, the flexibility and accuracy should not be reduced in this regard [5]. Therefore, the lAV goes the way of a maximally partly automated process, i.e. only such process steps are automated, which can be realized without a quality loss (or even with a quality gain). An example is the test execution. In contrast, the table filling is still an iterative process, which depends in particular for the definition of the target criteria and the boundary conditions - on expert knowledge.

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HARDWARE-IN-THE-LOOP SIMULATION AT THE TEST BENCH

The demands engine developers face today are increasingly complex. Improved driving comfort in conjunction with stricter legal emissions laws and shorter development times require new innovative technologies and forward-looking methods need to be developed and established. In this regard, the Hardware-In-the-Loop simulation (HiL simulation) at the test bench can make an important contribution to decrease the development times. The following sections give an overview on the goals of the HiL simulation as well as the configuration of the HiL test bench at IAV. Furthermore, the used physical models are introduced. Finally, some first measurements of the simulation at the test bench are shown. Goals

The aim of the HiL simulation at the test bench is the reproduction of the entire vehicle at an early development phase, during which a real test vehicle does not exist. In this way, vehicle testing can partially take place earlier in the whole development process. Usually, the vehicle and the power train including the ECU and TCU (Transmission Control Unit) are simulated, whereas the engine as hardware component runs on the test bench. Likewise, it may be suitable to integrate the ECU and TCU as hardware and only simulate the power train and the vehicle. In detail the following applications might be imaginable: •

• • •

Running of several driving cycles (NEDC, FTP, ... ) o Evaluation of the fuel consumption and the emissions o Comparison of several combinations of engines, transmission systems and vehicles Function development and test Calibration of ECU in conjunction with the TCU Simulation of hybrid vehicles

Test bench set up and interfaces

For this project, a highly dynamic engine test bench system of the company MTS® is used. The combustion engine is directly coupled with the electrical generator, Figure 3.

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Simulation models For simulation a vehicle model called Velodyn is used. This simulation model has been developed by IA V and is based on Matlab/Simulink®. The modularization is realized on the vehicle component level (Figure 4) and simulats the longitudinal dynamics of the entire vehicle. Beside the conventional vehicle components such as the engine, the gear box and the chassis, a virtual driver as well as the ECU and TCU are simulated. The model operates in real-time and is divided into several stages according to the level of detailing. This allows an optimal adaptation to the specific test situation. Moreover, the model can operated offline if the test bench communication interface is replaced by a Simulink® engine. Actually, the parameterization of the model is achieved via several initialization scripts. However, the process of a fast and simple parameterization of the entire model is still in the development stage. This process comprises, on the one hand, the acquisition of data from data sheets or specifications delivered by the manufacturer. On the other hand, measurements for comparable components are required to achieve a comprehensive calibration of the model.

81

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Figure 5 shows the curves of a small section of a simple driving cycle applied at the test cell for testing purposes. For this test a turbo-charged 4-cylinder diesel engine for passenger cars has been applied. Further important parameterization data is •

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The first diagram shows the velocity set point of the driving cycle. During the test the virtual driver included in the model attempts to keep the deviation between the actual speed and this set point as minimal as possible (diagram 2). In the applied model an automatic transmission has been used, so that the driver only has to operate the acceleration and brake pedal. The last diagram shows the engine speed set point transferred to the dynamometer and the actual engine speed. The curve characteristic also illustrates two gear shifts, which occur during the acceleration to 50 kmlh. The result of this test run shows a stable behavior of the closed loop control circuit. Moreover, the influence of several important parameters on the model behavior could be investigated.

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Figure 5 Snippet of a straight test cycle at the test bench CONCLUSION This paper describes the state of the art at model based calibration. The technical problems at steady state calibration are solved today, but modeling of dynamic behavior is still a challenge. One area of development is to bring simulation of the vehicle and transmission to the test cell to enable more calibration work at the test cell. Especially the question how detailed the models have to be and the speed and way of communication between simulation models and the test bed as well as the best usage of that test cell are very important topics. Also modeling techniques for dynamic behavior, like air charge and fuel dynamics are discussed in this paper. Models from signal theory and grey box models with a physical main structure and data driven sub models are presented.

ACKNOWLEDGEMENTS Thanks to the whole MD-G 1 team in IAV GmbH.

REFERENCES [I)

[2)

Ropke, K.; Fischer, M., 'Efficient Layout and Calibration of Variable Valve Trains', SAE 2001-01-0668, 200l. Ropke K. (ed.), Design of Experiments (DoE) in Engine Development II, Rennigen, Expert, 2005.

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[3]

[4]

[5]

[6] [7]

Winkler D., GUhmann c., Barzantny B., Lindemann M., 'Model Based Calibration of ECUs Using a Highly Dynamic HiL Test Bench System', Haus der Technik (HDT) - Tagung, Design of Experiments, Berlin, 2005. Mathiak D., KlOpper F., Components for a successful Engine Calibration at the Test Rig Using DoE Methods. Haus der Technik (HDT) - Tagung, Design of Experiments, 2005. Watanabe S., Ohata A., Ehara M., Butts K.: Future Calibration Process and Methodologies at Toyota. Haus der Technik (HDT) - Tagung, Design of Experiments, 2005. Knaak M, Schoop U., Ropke K. 'Models for transient air charge estimation', SAE 2005-01-0051,2005. Hendrick, E.; Luther, 1.: Model and observer based control of internal combustion engines, International Workshop on modeling, emissions, and control in automotive engines, Salerno, 2001.

©IAVGmbH

84

An Integrated Simulation Approach: Ricardo Transmission and Driveline Dynamic Simulation Library Felipe V Brandao and Peter A Harman Ricardo UK, DTS - Dynamics and Simulation

ABSTRACT A dynamic simulation library for Transmission and Driveline modelling has been developed using Modelica and has a user-friendly environment which is capable of coping with different levels of complexity and fidelity. The key advantages of models developed using the library are the reuse of the same model with several levels of fidelity without the need to generate additional models, and the easy maintenance of the parameter database throughout the project. An application based on a standard Automated Manual Transmission (AMT) is presented here in order to demonstrate some of the features of this library.

INTRODUCTION

The requirements of a dynamic simulation model can be very diverse, ranging from a simple model suitable for hardware in the loop simulation to a complex model capable of predicting high frequency transients. This typically results in a large number of models required to cover these different applications. Moreover, the level of duplication and reinvention of models among different departments and groups within the same organisation can result in significant cost and time penalty issues. An additional issue is the data management between different levels of model. This paper also discusses the benefits of this approach and shows a practical application of the library. Depending on the final application, the required model fidelity can change significantly. During a project, different models for the same components might have to be used and/or developed and for each one a different set of parameters could also have to be generated. These situations can generate a major data management issue which will require controlling different levels of model detail and data and assuring that all the information is kept coherent through out the entire project. A Transmission and Driveline Library has been developed in a Modclica [1] environment in order to create a simple way to address all those issues at the same time. The structure of this library is presented in this paper. Models created within the library can be configured to different levels of fidelity, allowing the same main model and set of parameter data to be maintained and used for a range of different analyses, from low fidelity HIL models to high fidelity shift quality and NVH models RICARDO TRANSMISSION AND DRIVELINE LIBRARY

The Ricardo Transmission and Driveline Modelica Library have been designed to be flexible and a user-friendly tool, suitable for use by experienced simulation engineers and non-simulation engineers. A suite of several models with different levels of fidelity has been developed and is available within the libraries. The use of replaceable elements

87

allows the model to be configured to the required fidelity, without changing the model structure or parameter data. This allows one model and accompanying parameter data to be used throughout the development process. Models can be created by the user by using pre-configured components such as gear pairs, shafts and bearings, allowing a transmission model to be quickly assembled. The user may also go further and extend the basic building blocks to create new system or subsystem models. There is also the possibility of integration with other Modelica automotive libraries using the Modelica Vehicle Interfaces Library. This allows high level whole vehicle simulation by simply selecting the required transmission and driveline models and defining the level of fidelity required for each e.g. low fidelity (control algorithm development, fuel economy) or high fidelity (shift quality simulation, driveline NVH). Structure

The library components allow the creation of manual, automatic, automated manual and dual-clutch transmissions, or other transmission concepts by the user. Those models are grouped following their main functionalities in the transmission and drive line systems. The main groups are: • Input: Dry launch clutches, torsional damper, torque converter, wet launch clutches • Output: Final drive, transaxle differential • Shafts and Gears: Shafts, Gear pairs • Engagement Devices: Synchronisers, Dog clutches, Wet clutches, CVT Variator • Selector Mechanism: Shift fork, detent, handset, shift cable • Bearing: Ball, taper, roller, needle roller Epicyclics: Sun, carrier, planet, ring The basic models created for the library are based on a package of 2-dimensional mechanical components, where motion and forces are resolved in rotational and axial translational degrees-of-freedom. Low-level models such as mass/inertia, compliances and sensors have been created and added to the library. The shafts in the transmission are specified using the shaft geometry and material where the geometry is entered using data records. A database of typical materials is also available within the library. Following a Finite Element approach, the shaft is discretised and the correspondent inertias, masses and compliances are calculated automatically from the shaft geometry and material. This allows changes to be made to the shaft design without the requirement to create complex parameter data structure. The bearing models have been developed in order to be either used as a rigid axial constraint, or as an axial stiffness allowing study of shaft endfloat. A number of bearing types are provided, and drag torque functions within bearings can also be selected and added. Among the engagement devices available within the library are the several models for dog clutches and synchronisers. For instance, depending on the application of the model, it is possible to select a very basic synchroniser or a more complex one where the geometry of the cones, coefficient of frictions, backlashes are taking into account. The selector package is comprised with several models which allows the user to build a model of a selector mechanism. A shift fork model is included to actuate the synchroniser and dog clutch models, and cables, detents, interlocks and barrel cams are

88

included to assemble selectors for manual transmissions or motorcycle or motorsport applications. Interfaces between the transmission model and the other systems from the vehicle such as engine and driveline are provided by specific Input and Output packages. For instance, clutches and torque converters are provided in the Inputs package and a final drive and differential are provided in the Outputs package. Planetary Gear Set or Epicyclics are also available within the library, including the basic elements and also the most common configurations such as Simple, Double, Ravigneaux and Simpson. The typical configuration for a Van Doome Continuously Variable Transmission is available within the advanced library as well as two typical hydraulic configurations that enable the user to quickly build its own CVT system. Replaceable models The replaceable model is the way chosen to minimise the effort to change, update and maintain several model complexity levels during the design process. Figure 1 shows schematically how the model fidelity ought to change depending on the stage of the design process where the simulation is carried out. For example, a high fidelity model is required when a component analysis for shift quality [2] or NVH [3] is performed and a low fidelity model is usually good enough to predict the fuel consumption during the European Cycle. System Optimisation

Component Analysis

Figure 1. Model complexity levels Maintaining just one model with configurable fidelity provides two main advantages: Multiple stages of modelling are not needed Parameter data can be maintained throughout the process As an illustration, Figure 2 shows the model developed for a Gear Pair where the level of complexity of the model can be resolved initially into two parts - the shaft model and the gear mesh model. There are two shaft models - rigid body and torsional compliant - and four different models for the gear mesh - ideal gear ratio, backlash-stiffness-damping, full hertzian contact, and impulse contact.

89

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Figure 3. Integration with Simulink AUTOMATED MANUAL TRANSMISSION An Automated Manual Transmission (AMT) has been chosen to illustrate how the developed library can be applied to a real automotive system. This transmission configuration has been selected because it illustrates the integration of the Modelica system with the Matlab/Simulink environment and tools. The AMT physical model has been developed using Modelica and the control strategy has been designed using SimulinklMatlab. Most of the modelling steps and the integration with Simulink is described in this section. Initially, a generic transmission template is selected. The transmission model has to be generated based on this template in order to be integrated with other sub-systems, such as Engine and Vehicle Chassis, without the need to redesign the interfaces.

90

Figure 4. Transmission Template shown in Dynasim Dymola

Using the drag-drop environment, the user has to chose which models he requires and links them in the correct order. There is no need to define the causality (whether the inputs are speeds and/or torques) because all models are acausal and when the model is finished, the Modelica compiler automatically assembles the correct set of equations for the final model. Figure 5 presents the system modelled in this work, in Dynasim Dymola [6]. On the left is the package browser, which lists the packages of the library. In the centre of the screen the model of the transmission is represented. This 5-speed Manual Transmission model is resolved into 5 main sub-models: • 1 dry clutch • 2 dual gear pair with synchroniser (1 SI_2 nd , 3rd _4th) • 1 gear pair with synchroniser • 4 bearings • 1 final drive gear pair

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Figure 6(a) illustrates a standard 5-speed Manual Transmission model connected to a shift mechanism, clutch pedal and an engine modelled as a source of speed with some noise!oscillation.

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Based on this system a basic simulation was carried out where the driver changes gears following a given pattern (N - 1st - 2nd - 3rd _ 4th _ 5th _ N _ 5th _ 4th _ 3rd _ 2nd 1st - N). Figure 6(b) shows the results of this simulation. In the top graph, it is represented the input shaft and output shaft speeds for the transmission. The other graph represents the x and y positions of the gear shift lever. An additional characteristic of this library is also represented in the same screen snap shot whieh is the 3D animation of the shafts and gears. This feature is particularly useful to ensure all the gears are in the right position and are functioning correctly.

(a)

(b)

Figure 6. Manual Transmission (a) model, (b) basic output simulation. After the Manual Transmission model have been tested and verified, an s-function is generated based on the model shown in Figure 5 and then exported to Simulink where it is linked to the control system develop in Simulink. The final model represented by Figure 7 can be divided into [green] • Engine [light blue] • Transmission (MT from Modelica) [yellow] • Vehicle chassis [white] Driver

Figure 7. AMT model in Simulink. The driver for this case study has been designed in order to follow the passenger car New European Driving Cycle (NEDC), in order to be able to analyse fuel consumption

92

and emissions for a small passenger car fitted with a standard 1.6£ Diesel engine. Some of the main parameters of this vehicle are listed at Table 1, based on the vehicle presented by Crewe et. at [4].

Table 1. Vehicle Main Parameters. Vehicle mass Transmission ratios

1400 kg 3.672 2.098 1.391 1.000 0.932 2.812 165/55 R13

Final Drive Tyre Engine Torque Power

188 Nm 105kW

One of the output graphs for the vehicle performance is plotted in Figure 8. In this figure, it is possible to analyse the engine speed and torque, the vehicle speed, which gear is selected, and the instantaneous fuel consumption. The vehicle cycle fuel consumption is 7.55 1/100km with emission within NEDC (New European Drive Cycle) limits (NOx = 0.197 g/km and Soot = 0.021 glkm), and additional vehicle performance characteristics are listed in Table 2.

Figure 8. Simulation plots outputs. Although, the setup of the vehicle for performance and/or emissions is not in the scope of this study, the overall performance and emissions are within the expectation for this size of vehicle.

93

Table 2. Vehicle Performance.

Test Results 0- 4 sec

45.8 m

0- 100 km/h

15.17 sec

50 - 70 km/h

2.96 sec

80 - 120 km/h

9.62 sec

0-60mph

14.23 sec

Max speed ---> 191.1 km/h in 111.37 sec

:§l

"

.Q

~ ())

~

0.3 0.2

Cii

"

'i5 0.1

:ECl

"0

....I

20

40

60

100

80

120

140

160

180

200

Longitudinal Velocity [km/h]

CONCLUSION In this paper, the Transmission and Drivetrain library developed by Ricardo has been explained and some of its features presented. A case study where an AMT has been modelled illustrates some of the capabilities of the library and has been used to demonstrate how it could be used to solve a real automotive modelling and simulation problem.

REFERENCE LIST Eimqvist, H., Mattsson, S.E., Otter, M., 'Modelica - A Language for Physical System Modeling, Visualization and Interaction', The i999 iEEE Symposium on Computer-Aided Control System Design, CACSD'99, Hawaii, August 22-27,1999 2 Davis, G., Donin, R., Findlay, M., Harman, P., Ingram, M., Kelly, D., 'Optimisation of Gear Shift Quality by Means of Simulation' , ATZ Worldwide 7-8/2004 Volume 106 3 Chae, C.K., Won, K.M., Kang, K.T., 'Correlation of Analytical Model with Experimental Results in Ranking the Gear Meshes in Order of Contribution to Rattle', The 32nd international Congress and Exposition on Noise Control Engineering, Korea, August 25-282003 4 Crewe, C.M. el al. 'Novel Techniques for Holistic Powertrain Optimisation', presented at iPDS 2006 integrated Powertrain and Driveline Systems Conference, IMechE, 14-15 June 2006, Ford Motor Company, Dunton, Essex. 5 Mathworks Matlab/Simulink, http://www.mathworks.com 6 Dynasim DymoJa, http://www.dynasim.com

© Ricardo UK Ltd.

94

A Powertrain Thermal Simulation Model By J. Hartland and A. Robertson, Jaguar Cars Ltd

1 INTRODUCTION The cooling system found in most automotive vehicles has remained unchanged over several decades. More recently, the cooling system has become an area of extensive investigation as manufacturers strive to improve fuel economy and reduce vehicle emissions, driven by environmental and legislative demands, see [1], [2]. An accurate model of the cooling system's behaviour is an essential tool if new technologies are to be implemented effectively. This work presents a thermal model of a typical cooling system found on modem vehicles. The model accurately predicts the warm-up response of engine, engine coolant, engine oil and transmission oil when given engine operating conditions and v.ehicle speed. The transmission model used also allows the prediction of transmission efficiency thus providing a useful indication of transmission losses as a function of oil temperature. The complete model is implemented in Matlab, Simulinkt thus removing the need for co-simulation techniques employing various different packages, see [3]. This not only reduces cost but improves on over all simulation time, taking only a few minutes to run a complete NEDC drive cycle. The cooling system model has been used during the development of an advanced 'Thermal Management' system for Jaguar and Land Rover Cars. The work is part of the PITSTOP project at Jaguar Cars Ltd. PITSTOP is a part public, part private funded project under the British Government run Foresight Vehicle programme. Partners on the project are Jaguar Cars Ltd, TOY NEL, Visteon UK, Gate UK and Prodrive UK. 2

COOLING SYSTEM LAYOUT

The modelling exercise is based around a 2004 model year Jaguar 3.0L Auto S-Type. The cooling system topology for this vehicle can be seen in Figure 1. The mechanical pump drives coolant directly into the engine. A wax thermostat at the pump inlet regulates the engine inlet temperature. The engine oil cooler flow is tapped-off the radiator return hose using an orifice. The transmission oil cooler is a separate oil-tocoolant heat exchanger integrated in the cold end-tank of the main radiator; there is no temperature regulation in the transmission oil cooler circuit. The engine coolant circuit splits into each cylinder bank where it traverses the cylinder coolant jacket and then returns via the head as shown in Figure 1. Due to its low impact on the overall thermal performance of the system, the throttle heater normally found on the vehicle has not been included in this study. Also, although a cabin heater is shown, this element is not discussed during this study as it is beyond the scope of work presented.

t

Matlab® and Simulink® are registered trademarks of The MathWorks, Inc. of Natick, Massachusetts

95

Header Tank (HT) Cooling pack (R)

TRin Bypass (B) Coolant out

I I I I I

I I I I I

I I I I I

I I I I I

Coolant in ,-,---,-',...'--,-',...'--'-'

Trans Oil Cooler

Figure 1 Cooling system layout

3 FLOWMODEL As mentioned earlier, the complete model is implemented in Matlab/Simulink blocks thus avoiding co-simulation often employed in studies of this type. Simulink is a block diagram based simulation environment in which causality must be determine by the user offline. For many systems this is satisfactory such as control system algorithms however descriptions in a closed network form such fluid pipe work or electric circuits must have the governing equations resolved offline and meticulously entered. This is often tedious particularly when frequent changes in the topology are necessary as each iteration may generate extra loop variables and widely differing equations. A technique has been employed here that aims to alleviate this issue by entering network equations using a systematic approach. It has been shown to speed-up the implementation of network changes and greatly reduces susceptibility to human error.

(b) Loss coefficient calculation

C!J

=Parallel

8

=Series

0 = Loss coefficient

Figure 2 Calculation of total loss coefficient. (a) Topological representation. (b) Loss coefficient calculation Figure 2( a) shows a topological representation of the cooling system of Figure 1. A total loss coefficient can be calculated in a systematic way by combining parallel and

96

series combinations as shown in Figure 2(b). Network changes can be quickly implemented by redrawing Figure 2(b) and directly translating to Simulink. 3.1 Pressure drop/flow rate calculations In order to realise Figure 2(b) it is necessary to define the series and parallel combination relations. Equation (1) gives the pressure drop (M) through two devices/pipes in series M=(C pa +CPb )·Q2 (1) Equation (2) gives the pressure drop through two devices/pipes in parallel.

M-[- (JC:: +.[C;;j j.Q2

(2)

Cpa ·Cpb

Also, the flow through branch 'a' (Qa) of a pair of parallel devices/pipes is given in Equation (3). (3) In Equations (1) to (3) CPa and C pb are the loss coefficients through branches 'a' and 'b' respectively andQis the total flow rate. Density changes have been disregarded as the coolant is incompressible and density changes with temperature are considered negligible. By combining Equations (1) to (3) according to Figure 2(b), a total loss coefficient, K,ys , can be determined. 3.2 Pump calculations Supplier pump pressurelflow and power data has been partially non-dimensionalised into parameters Q/ N (where N is pump speed in rpm), M/ N 2 and W/ N 3 (where W is the pump shaft power in Watts). These are fitted to polynomials from which the pressure rise across the pump can be calculated for any given pump speed and system flow rate using Equation (4).

~i

=C J*Y +C j*Y +C1j*)+C 3

2

OM

(4)

where C iM are the polynomial coefficients. The pump power requirements can also be calculated using Equation (5) where Ciw are the polynomial coefficients.

:; =C3w(~Y +C2w(~Y +Clw(~)+Cow

(5)

The coolant pump power usage is also used as an addition onto the total heat supplied to coolant. This value can be as high a 7kW for the engine used in this model. 3.3 Solution method Given the total system loss coefficient (K,y, ) calculated above, the system pressure drop at the current flow rate, Qp, is calculated, along with the pressure rise across the pump. The difference between the pressure rise across the pump and the pressure drop around the system is used to accelerate the fluid within the cooling system. Equation (6) shows the final solution scheme which is integrated numerically using Simulink

97

(6) Here

ISYS

represents the lumped coolant inertia estimated using volume information.

Once the pump flow rate is known, the individual branch flow rates can be determined using a similar approach as above with Equation (3).

4 THERMODYNAMICS MODEL The heat used to warm the coolant and engine is calculated for each source and sink within the system. The individual heats are imposed on the coolant within the respective device and the resulting temperature transported around the external cooling system.

4.1 Engine heat flux modelling The calculation of the engine heat flux is based around a model called PROMETS developed by Nottingham University. A detailed description of PROMETS is beyond the scope of his paper and the reader is directed to [4] for more information. For this model, PROMETS has been adapted to work in conjunction with the external cooling system described throughout this paper. The total heat from gas-side is calculated using the correlation developed by TaylorToong, see [5]. This correlation uses air/fuel ratio, AFR, fuel flow rate, m!, engine bore diameter B and an empirical correlation for average gas side temperature, Tg,. based on the equivalence ratio.

(7rB JkB 2

HG =10.4 -4-

g (

Tg,.-Te ) Reg0.75

(7)

Where the effective gas-side Reynolds number is determined by

4m! (I+AFR)

Re

= --"---g

7r B f-lg

(8)

The engine is simulated as a single cylinder, which is then extrapolated to the correct engine size. Both the head and cylinder are modelled as 54 discrete elements each with a pre-calculated mass and surface area. The total gas heat flux is then partitioned such that a representative percentage is delivered into the various cylinder inner surface areas. Each engine metal temperature is then calculated using,

Mcp dTm =HG-He-Ho (9) dt Where H 0 is the heat flux to engine oil and He is the heat flux to coolant. The heat flux to coolant at each element is then determined by Equation (10) He = h(Te -TM ) (10) Where h is the total heat transfer coefficient itself calculated as the superposition of conductive, convective and nucleate boiling as shown in Equation (11). h

=

[(Ts -Tsat )] [h conv + h] cond + hnud,boiling (Ts - Tc)

(11)

The conductive term reflects the local geometry of the coolant passage as shown in Equation (12) where kcool is the thermal conductivity of coolant and L1x the cross

98

sectional area. This transport mechanism is important for systems that are able to stagnate coolant. =

h cond

kcool

(12)

&

The forced convective term is based on the Dittus-Boelter equation shown in Equation (13). h

= 0 023 ReO. 8 PrO.4 _Dk

(13)

con.'

The nucleate boiling term is calculated using the Chen model given without explanation in Equation (14). For more information see [6]. k 0.79 0.45 0.49 I Cp,1 PI h 0 00122f1T 0.24 An 0.75 8 [ (14) nuel,boiling = . sat '-"i'sat 0.5 0.29 ( \0.24 a l PI f1h lg P g J The total heat-to-coolant, (10), is then used to determine the coolant warm-up in the block and head as discussed below.

1

4.2 Engine heat flux to coolant It has been assumed that the heat flux into the block water jackets is insignificant

compared to the heat flux via the heads. As such, the block is treated as a variable time delay upstream of the heads. The time delay is calculated from the volume of coolant in the block (Vblack ) and the engine coolant flow rate ( QE ) using Equation (15). f1t

= block

~Iock QE

(15)

The time delay in the heads is calculated by treating the water jackets within the heads as a mass of water ( Me _ head) into which heat (He) flows from the surrounding metal at a rate determined by PROMETS using the engine speed and load, the metal and coolant temperatures and the coolant flow rate. Heat is lost from the body of water as coolant at the bulk temperature (~h ) flows out, and heat is also gained through the flow of coolant from the block at the delayed inlet temperature (TcUn )' Equation (16) is integrated numerically within Simulink to give the coolant temperature within the head for each cylinder separately. Mc_headCp

d~h

= pCp

QA~_in -~J+Hc + Hpump

(16)

To allow for the coolant warming up within the engine and the coolant within the block, the temperature returned to PROMETS for determining the heat flux into the coolant is taken to be the average of the inlet and bulk temperatures within the cylinder head. The term H pump accounts for the heat added by the pump derived from Equations (4) and (5). This is made up of both the inefficiencies within the pump due to the pump internal flow and the mechanical losses such as bearing friction (the difference between shaft power and fluid power) and the fluid power as it is lost through the system. The second part of the heat added by the pump, due to the pressure losses through the cooling system, should be distributed through the model but for simplicity the total heat is added here and is assumed to be the pump shaft power.

99

4.3 Radiator heat flux The radiator is treated as two variable time delays similar to the treatment of the engine block. Half of the coolant volume within the radiator is assumed to reside in each end tank, giving one delay before the radiator core and one after. The core is assumed to contain no coolant and the coolant temperature change across the radiator core is given by Equation (17) HR = P cp QR (7;._oul -7;. _in) (17) The heat flux from the radiator is taken from a lookup table of supplier data. This map gives variations of heat transfer coefficient (W/m 2/K) with coolant flow rate and airflow rate. The coefficient is scaled by the radiator panel area and the inlet temperature difference (7;. _in - T.ir ) to give the heat flux (H R)' Air flow rate is assumed to be linearly related to vehicle speed and the scaling factor has been calculated by matching the heat transfer to data from 100 km/h full scale wind tunnel tests of an X202 3.0 with the vehicle radiator attached to a cooling rig. 4.4 Oil cooler heat flux The oil cooler heat transfer is also calculated from a function based on supplier data. The oil flow rate is taken from a lookup table of oil flow rate variation with oil temperature and engine speed. The oil cooler is treated in the same way as the radiator, with a variable transport delay upstream and downstream based on the length and diameter of the coolant pipe work to and from the oil cooler. The heat flux is calculated from the oil and coolant flow rates and inlet temperature difference, and the temperature change in the coolant is calculated in the same way as for the radiator. The oil temperature is taken from within PROMETS and the heat flux from the oil to coolant (or coolant to oil) is passed back to PROMETS for use in its internal oil heat transfer calculations.

4.5 Pipes Pipe joints are split into two types. Where fluid enters through a single inlet and leaves through more than one outlet, the temperature of the fluid leaving the junction is the same as that entering the junction. Where fluid enters through more than one inlet and flows out through a single outlet, then the enthalpy of the fluid leaving the junction must equal that of the fluid entering. Equation (18) is used to calculate the temperature of fluid leaving such a junction. The transport delays associated with pipes are captured using the Simulink variable transport delay block. The delay time is calculated using the coolant volume and flow rate within the respective pipe in the same way as the engine block described earlier.

T.UI =...!.:i=,,-\_ _

(18)

LQi i::::l

4.6 Thermostat model A simplified wax thermostat model has been used for this study. It employs a typical, measured thermostat opening characteristic as a function of coolant temperature circulating past the wax pellet. This curve is shown below in Figure 3(a). Hysterisis in 100

the thennostat has been neglected for this study. The thennal response of the wax pellet has been modelled using a simple lag with a time constant of 6 seconds. This corresponds to similar reported characteristics as can be seen in [7]. 4000 3500 ro 3000 eo. 1: 2500 OJ '(3 If: 2000 OJ 0 1500 u

"'E Ul ::§ 0.8

ro

iii 0.6 0

E OJ J!:

0.4

I/) I/)

0.2

0

--'

0 0

40

80

120

160

1000 500

o0

200

(a) Temp [degC]

0.2

0.4

0.6

0.8

(b) Thermostat lift

Figure 3 Thermostat characteristic. (a) valve lift and (b) loss coefficient The loss coefficient is detennined from the thennostat opening and is split into a separate loss coefficient for each branch based on the separate branch flow rates and pressure drops. Figure 3(b) shows a typical loss coefficient for the radiator branch. This is again taken from supplier data and is described by a polynomial. 4.7 Transmission model The transmission efficiency is calculated from a set of mapped, steady state data. The maps contain transmission efficiency in each gear over the full range of input shaft speeds and torques at 40°C, 60°C and 80°C. At each operating point and in each gear, the efficiency, 1]r' is correlated against temperature, T,rans' using Equation (19).

= K N,r,G In{TtranJ + CN,r,G

(19) For each gear, G, the values of K and C are mapped against input shaft speed and torque (N, r). The values of K and C are interpolated from the maps at the instantaneous speed and torque for the relevant gear position. The efficiency is then calculated at the instantaneous temperature. The ratio of engine and transmission thennal inertia has been calculated by comparing wann-up data from a vehicle with the transmission oil cooler disconnected and a vehicle with the transmission oil cooler connected. Heat rejection from the transmission oil is calculated using Equation (20). Hr = Nr{1-1]T)2;r/60 (20) Transmission warm-up rate is solved using Equation (21) where HTOC is the heat rejected through the transmission oil cooler and I r the transmission thennal inertia. The transmission oil cooler heat exchanger is modelled in a similar manner to the engine oil heat exchanger described earlier. 1]r

H r - H TOC- H cony I rdT,rans (21) --= dt The Heonv tenn accounts for the heat convected from the gearbox casing and is approximated by Equation (22) where Ceonv is a scaling factor and v is the vehicle speed. (22)

101

5 RESULTS The ability of the model to predict engine, engine oil and transmission oil warm-up can be seen in Figure 4. The model has been presented with the necessary measured vehicle operating conditions described above over a standard NEDC drive cycle. As can be seen, the model has accurately predicted both the lubricant and coolant temperatures. The predicted engine metal temperatures are shown but not compared with measured data due to availability. Since the model uses a simple thermostat model, the predicted coolant temperature lacks the slight excursions found in the measured data. 140 Predicted engine metal temp (rreasured data unavailable)

~

120 [) 100 OJ (J)

~

~

80

::J

~

(J)

Cl.

E

60

~

40

Transmission Oil temp

"-

20

Vehicle speed

o

200

400

600

800

1000

1200

lime [Sees]

Figure 4 Calculated warm-up of metal and fluids compared with measured data. Calculated values (-), measured values (--)

The transmission model produces a prediction of efficiency over the drive cycle as discussed previously. This prediction can be seen in Figure 5 below. The transient behaviour seen within the prediction is due to gear changes to neutral. These naturally occur during gear transitions and the model has assumed 100% efficiency during the neutral phase. It is interesting to note that at start-up when the transmission is cold, almost 20% of the input mechanical energy is lost. Also, as the transmission oil begins to warm-up the efficiency improves until reaching almost 90% at the end of the drivecycle.

102

,

100 90 ~ 0

80

u c:

>-

70

'(3 ~

60

~

~~ ~

~

1'\.

~

U

~~~ ~ r,<,·.;J

( ..,/

Q)

UJ

c: 0

'iii

""i.;.· . ;

l'.

50

'\

tI)

'E 40 tI)

V

c:

~ I-

,V'

~',

30

"1

.~

c"',

20 ".

10 0

o

J\ 200

400

:'"

Vehicle speed

.;,

600

800

1000

1200

lime [Sees]

Figure 5 Transmission efficiency over NEDC drive cycle

6 CONCLUSIONS This work has presented a thennal powertrain model completely described using native Matlab/Simulink blocks thus removing the need for co-simulation techniques. The model is able to predict the coolant, metal, transmission and engine lubricant wann-up. It is also able to predict the transmission efficiency during transmission oil wann-up. Results have been presented showing the capabilities of the model over a NEDC drive cycle. The model has accurately predicted lubricant and coolant wann-up. In order to predict the slight engine coolant-out temperature fluctuations after thennostat crack, it would be necessary to elaborate on the thennostat model. This is the subj ect for further work. The model also predicts transmission efficiency. Results have been presented for the transmission efficiency over the NEDC drive cycle. Surprisingly, it has been shown how the transmission can dissipate around 20% of the mechanical input energy when cold. The purpose of this model is to allow cooling system designers to embrace modem design techniques in which parameterised optimisation can be perfonned on cooling systems. Alternative topologies are easily implemented and new technologies such as electric pumps and valves can be studied without rigorous re-design of the model. 7 REFERENCES [1] Farrant, P.E, et al. The Application of Thermal Modelling to an Engine and Transmission to Improve Fuel Consumption Following a Cold Start, VTMS, Toronto, 2005, SAE Paper Number 2005-01-2038 [2] C. Roberto, et al. A Fully Transient Model For Advanced Engine Thermal Management, VTMS, Toronto, 2005, SAE Paper Number 2005-01-2059 103

[3] Puntigam. W., Thermal Management Simulations by Coupling of Different Software Packages to a Comprehensive System, VTMS Toronto, 2005, SAE Paper Number 2005-01-2061, [4] PJ. Shayler, A Modelfor the Investigation of Temperature, Heat Flow, and Friction Characteristics During Engine Warm up, VTMS, SAE Paper 931153 [5] CF Taylor and TY Toong, Heat Transfer in Internal Combustion Engines, ASME Paper 57-HT-17, (1957). [6] lC. Chen, A Correlation for Boiling Heat Transfer to Saturated Fluids in Convective Flow, , Ind & Enging Chem- Process Des and Dev, 1966, 5(3), 322-329 [7] Nelson, V.A. and Robichaux. lD., A model to simulate the behaviour of an automotive thermostat,. VTMS97, Indianapolis, Indiana, 1997, SAE Paper Number 971814

104

Novel Techniques for Holistic Powertrain Optimisation A Hybrid Vehicle Case Study C M Crewe, J Seabrook, F Brandao Ricardo UK Ltd S P Edwards Ricardo Deutschland GmbH

SYNOPSIS The matching and calibration of a vehicle powertrain (engine and transmission) is often achieved via independent activity. The engine calibration is test bed oriented initially; the transmission ratios, calibration and launch device (clutch or torque converter type) are often selected with minimal consideration of emissions and fuel economy. Frequently it is only during the vehicle pre-production prototype development phase that combined activity is commenced. The inter-dependence of engine and transmission calibration makes this an iterative process; with adjustments made to the engine calibration in response to changes in transmission control calibration - and vice versa. This is time consuming and costly: future vehicle programmes require shorter development cycles such that reduced facility and resources are needed. A new process is presented whereby a holistic (whole system) approach is applied to powertrain optimisation. The new process yields significant improvements in vehicle emissions and fuel economy. Novel techniques for ratio selection, shift strategy optimisation and engine calibration using Design of Experiments techniques are extended in this application of a hybrid powertrain.

INTRODUCTION Vehicle powertrain teams frequently develop the attributes of the vehicle for which they hold responsibility with a high degree of independence, with sequential optimisation of the engine calibration, transmission ratios and shift point calibration. During the development process for a new powertrain application the transmission ratio setting and shift point calibration process (for automated transmissions) can be based upon steady state engine data (e.g. brake specific fuel consumption versus engine speed and load). This data is acquired early in the engine development process and iterations can then occur whereby transmission modifications require engine calibration modifications, which in tum require further transmission modifications. Any potential synergy, between the engine and the transmission calibration, is lost since the transmission ratios and shift points are selected for an existing engine calibration, rather than one optimised for the developed application. A combination of simultaneous engine and transmission calibration potentially offers benefits, for example improving fuel economy and reducing development time via the reduction of iterative development steps [1]. The concept of combined engine and transmission calibration and its potential benefits are illustrated in Figure 1. Suppose one start with a cycle simulation using fixed transmission settings, BSFC and BSNOx maps; this is shown as a star in the figure. Optimising transmission ratios for the new application would give a range of possible combinations of NOx and fuel consumption with the fixed engine calibration (solid line). Alternatively, if the nominal 105

ratios are retained, a range of solutions can be found by adjusting the engine calibration (dotted line). The third method, optimising engine calibration and transmission ratios together, could produce a superior set of candidate calibrations and ratios (dashed line) this is the basis of the holistic approach. The potential of this approach was demonstrated in previous studies [2], with demonstrable benefits in both fuel economy and emissions. For the study reported here the extension of the method to consider a hybrid application was made.

*

Fixed Engine Calibration, Nominal Ratios Fixed Engine Calibration, Optimal Ratios Optimal Engine Calibration, Nominal Ratios ---Optimal Engine Calibration & Ratios -

Cycle NOx Figure 1 - Illustration of Potential Benefit from Simultaneous Optimisation The diesel engine hybrid application was chosen with some care, hybrid vehicles are generating increased interest and popularity: •

There is societal interest in reducing fuel usage and reducing C02 emissions that has lead to pan European political pressure to significantly lower C02 emissions

•

Due to taxation policies fuel costs are relatively high when compared with other markets

These factors suggest that highly fuel-efficient hybrid vehicles should be attractive in the European passenger car market. However, Europe has led the world in developing refined, high performance diesel vehicles as a route to lower C02, whilst the current range of US and Japanese gasoline hybrids offer only marginal benefits in C02 and even less in volumetric fuel consumption [3]. Logically, the European market may offer the best opportunities for diesel based hybrid systems. Due to their high torque output, diesel engines require a smaller electrical machine than the same sized gasoline engine, though opportunities to reduce fuel consumption are less than for gasoline products since the base engine efficiency is higher, particularly at low loads. The added cost of high torque electric motor to a diesel engine makes this a much more expensive powertrain than the gasoline equivalent. However, if fleet C02 reductions are needed, there are not many other technologies that could significantly improve the efficiency of diesel engines cost effectively, for this reason, the diesel hybrid has some merit, indeed a well optimised diesel hybrid powertrain may be able to offer a strong challenge to the 106

gasoline hybrid market. This could be further considered to be an additional option for consumers as an ultra low fuel consumption vehicle with commensurate emissions capability.

OBJECTIVES The principal objective of this study was to demonstrate the value of the simultaneous optimisation of the powertrain (engine and transmission) but with added dimension of a hybrid application (e-machine) within a small sports utility vehicle (SUV). This optimisation was to be for the best emissions and fuel consumption trade-off whilst maintaining battery state of charge over a cycle. Two optimisation approaches were compared: a conventional simulation-based approach using fixed emissions and fuel economy maps for the engine, and a variation on this process whereby DoE models of engine emissions and fuel consumption are employed [1].

APPLICATION AND APPROACH In order to demonstrate the method, the application chosen was a parallel hybrid SUV with a 1.7£ Diesel engine combined with an automated five speed automated manual transmission [1]. The engine had previously been applied to a passenger car with a conventional manual transmission. The DoE models generated during that calibration process were utilised for the simulation process. The vehicle had a conventional single dry clutch and the transmission was the same as a conventional manual transmission. Automation was provided by an electric clutch actuator and rail actuation systems similar to those applied in many European Automated Manual Transmissions (AMT). The electric machine chosen was rated at 30 kW with the target for end of cycle state of charge (SOC) set to that at the start of the test, 60% in this case. Torque to the wheels could be supplied via any combination of engine and electric motor within the capacity of those units. During braking, the motor acts as a generator providing charge to the battery. All of the simulations were based on the passenger car New European Drive Cycle (NEDC). The emissions targets were set to the Euro 4 limits of 0.25 glkm NOx and 0.025 gIkm PM (Particulate Matter). Calibration development for the purpose of improving fuel economy/emissions is often to the detriment of driveability and NVH. To ensure that this did not occur, constraints were applied in the process to maintain the operation of the engine (during the simulation) above pre-defined speed limits in each ratio.

PROCESS The conventional process for powertrain development is illustrated in Figure 2: fixed emissions, fuel economy and transmission efficiency maps are applied together with powertrain models to a simulation of the drive cycle. The calculated emissions, fuel economy and battery state of charge are then tested for a suitable result and the powertrain variables manipulated to improve the results. During this process the engine maps remain fixed. In the holistic process (Figure 3), the inputs are the DoE models of engine performance as well as the transmission and electric machine variables. The outputs are cycle predictions, optimum gear ratios and/or shift maps plus an engine calibration specifically optimised for these ratios and/or shift map.

107

Fixed Engine maps (BSFC etc)

Drive Cycle Simulation

14-------0ptimise Variables

Figure 2 - Conventional Powertrain Optimisation Applied to a Hybrid Powertrain Variable Engine maps (Timing. EGR etc)

14-------0ptimise Variables

Drive Cycle Simulation with DoE Models

• ' ~

,~

Battery State Of Charge

,

14------0ptimise Variables

Figure 3 - Holistic Approach to Powertrain Optimisation Applied to a Hybrid Powertrain Full cycle simulation The process is based on full cycle simulation, whereby the vehicle fuel consumption is evaluated for each second of the drive cycle. There are two variations of this type of cycle simulation - the forwards and the backwards methods. For the forwards method, a driver model is used to adjust the driver demand in order to maintain the correct speed and load as the cycle is driven. This is a very similar method to that found in a nonnal vehicle, where the driver is giving an input, the vehicle delivering a response and the driver then modulating the input to match the target vehicle speed, for example during a drive cycle. However, the preferred method was the backwards method where the speed and load are pre-defined by the drive cycle - this obviates the requirement for a (driver) controller model.

108

The backwards method is significantly faster than the forwards method and evaluation speed was very important during optimisation because of the large number of variables that were considered. The Matlab TM code used for the holistic optimisation is able to make cycle predictions using the DoE models in a few tens of milliseconds. Minimising function evaluation time is critical in achieving a practical result. Cycle predictions of a few tens of milliseconds produce total optimisation times of the order of tens hours when both the engine calibration and transmission variables are optimised.

Engine Variables There are several options for defining the engine calibration variables as inputs into the optimisation process. Since, in a normal engine calibration, the ECU parameters are mapped against engine speed and load, the effective number of variables to be optimised is large, with 50 to 100 variables not being unusual (depending on the approach taken and accuracy required). In this study, the 5 ECU parameters were represented by 80 optimisation variables, with the optimised values being sufficient in number to generate initial ECU maps covering the region of operation encountered by the engine during an NEDC. A constraint function was applied to the ECU maps during optimisation to ensure the resulting maps for timing, MAF, rail pressure and boost were "smooth" and, therefore, driveable. The map smoothness constraint is a generic function created by the DEPE Consortium [5]. ,-.-_ _ _.--_-,-_.,--_..,...-_--,--_--,--_,--_,--_,.-_ _- , - - , . - - , Response

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1000

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Inj liming (BTDC) Pilot Timing (deg) Rail Pressure (bar)

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120

160

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Airlfuel Ratio Boost Pressure (kPa)

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Figure 4 - Example Speed-Load DoE Models for NOx, PM and Fuel Consumption The DoE models (Figure 4) were "speed-load" or "global" models, which means that, in addition to the models having the standard calibration variables (injection timing, MAF, pilot injection timing and quantity, rail pressure and boost pressure) as inputs, the models also had engine speed and torque as input variables. The models were created from a data set of 340 test points devised in an optimal Latin Hypercube design. Figure

109

4 shows a "swing" of each model variable in tum, with other variables set to their midpoints. The dotted lines are 95% confidence intervals about the prediction. With the speed-load models, predictions of emissions can be made for any operating condition within the operating region covered by the models. There was one DoE model for each engine output response (NOx, PM and fuel consumption) and each covered the operating range of 1000 to 3000 rev/min and 0 to 250 Nm and the same set of engine control parameters. This speed-load region is sufficient to allow engine calibrations to be generated for many different vehicle applications over the NEDC and other vehicle test cycles. Speed-load models have a strong advantage over "key point" models when applied to combined engine and transmission optimisation because as ratios and shift maps change during optimisation, the speed and load points encountered during a cycle change too. E-machine variables The e-machine torque output was mapped against engine speed and total torque demand, with additional maps based on SOC. All map sites were treated as independent optimisation variables with a constraint function designed to ensure smooth maps. Transmission variables Many techniques are applied to the setting of transmission ratios: often the activity is restricted to an evaluation of available ratio sets (in terms of performance and fuel economy) and then selecting the set that is most applicable to the requirements of the vehicle programme. If a new transmission is being developed then more scope is available for the setting of the gear ratios. Frequently, an experience-based process is used with recourse to simulation methods as required. However, increased emphasis on powertrain development to improve fuel economy necessitates that a more structured approach is used for gear ratio selection. Additionally, the electric machine in the hybrid powertrain must be considered. Whichever process is adopted, first gear is set dependent on the gradient (typically I in 3 as limit) requirements and traction limit of the vehicle; this is shown in Figure 5.

Figure 5 - Vehicle on Grade First gear ratio = Mass x g x Sin a x ([ Rr ]) Tmax x Teff x FDR

[Equation 1]

Frequently, the gear ratio is set to give an overdrive top gear ratio, thereby allowing good fuel economy for high speed cruising, with low engine speeds also aiding refinement. The remaining ratios, for the intermediate gears, are selected to deliver a smooth transition between under and over drive. The selection of the intermediate gears is an important aspect for the performance feel of the vehicle. Several approaches are applied, for example geometric gear progression, where the choice of upper and lower bound limits on the engine speed are 110

defined first and then the engine speed is plotted against the vehicle speed to yield a line, which can be extended to the origin to give the gradient for the next gear. Gear ratios arranged in geometric progression are shown in Figure 6, this type of ratio selection is normally only found in commercial vehicles [4], a typical passenger car progression is indicated in Figure 7, where there is less overlap (speed range) in the lower gears and increased overlap in the higher gears The overlap gives a satisfactory operation of the transmission in more than one ratio, so that for high load cases, for example mountain climbing, the driver or transmission controller is not required to change at high frequency. The vehicle speed range for a particular gear is required to encroach upon the neighbouring gears above and below 5% limits. Since it is possible to design constraint functions in order to set the intermediate gears, an optimisation approach is desirable.

Vehicle Speed (mph)

Figure 6- Geometric Gear Progression

7000 6000

E

,g;

..,..

sooo 4000

8.

.

I/)

c '6J c W

3000 2000 1000

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

Vehicle Speed (mph)

Figure 7- Typical Passenger Car Ratio Progression

111

160

170

In the study reported here, the transmission ratios were treated directly as optimisation variables. To retain flexibility over the combination of final drive ratio and individual gear ratios, the overall gear ratio (OGR) for each gear was used as the optimisation variable. Ratio Spacing

Issues can occur during optimisation whereby the gear ratios selected mean that one gear ratio is very close to the next gear ratio: this is a poor practical solution. This issue was avoided by the use of another simple constraint function. The constraint function forced the progression (the ratio of one gear ratio divided by the ratio of the next gear) to be kept within a given range, and the progression to increase monotonically when going through the gears. Shift point calibration

In Figure 8 a typical automated manual transmission shift strategy calibration is illustrated. The solid lines indicate up shift conditions, with the driver or controller shifting up as the vehicle speed and/or demand are increased. The dashed lines indicate down shifts, where the converse is the case.

20

40

60

80

100

120

140

160

180

200

Vehicle speed (kph)

Figure 8 - Example shift map calibration

Typically, for a vehicle manufacturer, shift map design and calibration is a key brand indicator. For example, a calibration can be manipulated to make the vehicle feel sportier by selecting lower gears at points on the speed-demand map. Alternatively, the calibration could be manipulated to improve the vehicle fuel economy by up shifting, to increase the load on the engine. Usually the automated transmission controller has adaptive modes designed to cater for changes in the driving environment, e.g. grade, altitude and traffic situations. This can have several manifestations, but these are primarily shift point adaptations and map switching. For hybrid applications, modes are also specifically designed for electric

112

only running (ZEV mode), regenerative braking and mixed hybrid running to complement those normally used for non hybrid applications. There are several options for including the shift map in the optimisation process. The option that adds fewest optimisation variables is to treat shift map optimisation as a sub-routine within the optimisation loop. In effect, the shift map is updated to match specific fuel economy and emissions objectives at each iteration of the optimisation process. RESULTS Fuel Economy and Emissions Results from the optimisation process are displayed in Figure 9; the asterisk shows the small passenger car with a conventional optimised powertrain [1]. This vehicle was the donor vehicle for the SUV powertrain with a "carried over" engine calibration (and, therefore, the fuel, NOx and PM maps). The engine calibration, shift schedule and ratios for this vehicle had been optimised to give the best fuel consumption whilst meeting NEDC emissions levels. This gave a fuel consumption of 6.38 VlOOkm, with NOx at the legislated limit of 0.25 and PM at 0.014 gIkm (the legislative limit is 0.025 gIkm) respectively. It should be mentioned that in a real world case it is normal to develop the vehicle to achieve engineering targets that are below those legislated limits. Transplanting the powertrain into the SUV application yields a fuel economy of 8.39 tIlOOkm with NOx considerably in excess of the legislative limits, at 0.43 gIkm and PM safely within the limits (square). This is largely as expected, due to the increase in the inertia class of the vehicle. Simply transplanting hybrid components into an existing vehicle platform will not necessarily yield the expected improvements in emissions and fuel economy. Addition of the electric machine again adds load but helps the fuel consumption via use of the motor in areas where it is advantageous to do so with respect to the internal combustion engine. The holistic process is to optimise engine calibration, transmission ratios, shift strategy calibration and the electric machine simultaneously. This offers the best potential for the system and the result is indicated by the pareto curve for the SUV hybrid. The curve indicates a range of solutions, with only two below the legislative limit, hence the best fuel economy is designated as that achieved with NOx at 0.226 gIkm. The fuel economy is 5.48 VlOOkm with Particulate matter at 0.021 gIkm and the battery state of charge was maintained at 60%. This is a significant improvement from the nominal SUV vehicle at 8.39 1ll00km and demonstrates the potential for this type of approach. In addition to the predicted results, which can be used to help in the planning phases of vehicle development, the simultaneous optimisation process delivers an initial engine calibration, transmission ratios, shift map and strategy for the use of the electric machine. In essence the process is applicable throughout the development cycle. FC (UlOOkm) 6.38 8.39 5.48

Small Passenger Car NominalSUV Optimised Hybrid SUV

NOx (g/km) PM (g/km) 0.25 0.430 0.226

Tablet - Simulation Study Results

113

0.014 0,018 0.021

•

I I I

)k

)I( Small

Car Start Point

.SUV • Optimised SUV

0.00

0.10

0.20

0.30

OAO

0.50

0.60

0.70

0.80

NOx [g/km]

Figure 9 -NEDC Optimisation Results CONCLUSIONS

A process for the simultaneous optimisation of a hybrid powertrain (engine plus transmission) system has been developed and applied to an SUV hybrid powertrain. The case study has demonstrated that it is possible to achieve improvements in fuel economy over the conventional SUV, whilst complying with legislative limits for emissions. Many different parameters were manipulated for the engine, transmission and the application of the hybrid machine, that allowed calibration and control strategies to be developed before the vehicle development process began. Simultaneous approach to engine and transmission calibration had been demonstrated previously with the case study showing clear benefits over independent activity. The holistic method produces a demonstrable improvement in drive cycle fuel economy and emissions compared to the conventional process. The extension of the process to that of a hybrid application underlines the strength of the process and potential that exists for this type of approach to future hybrid and non-hybrid applications. NOTATION AND NOMENCLATURE q.

AMT BSFC BSNOx BSPM DCT DoE

Gradient angle Automated Manual Transmission Brake specific fuel consumption Brake specific oxides of nitrogen Brake specific particulate matter Dual Clutch Transmission Design of Experiments

114

E-Machine FDR GR MAF NOx OGR PM Rr SOC SUV Tmax Tell

Vmax ZEV

Electric Machine Final Drive ratio Gear ratio Mass airflow Oxides of nitrogen Overall gear ratio Particulate matter Rolling radius Battery State of Charge Sports utility vehicle Maximum engine torque Transmission Efficiency Maximum vehicle speed Zero Emission Vehicle mode

REFERENCES 1 C M Crewe, J Seabrook and S P Edwards, 'Holistic Powertrain Optimisation - A New Approach', Engine and Transmission: Progress for the next ten years? Paris SIA Conference, May 2005 2 Design of Experiments for Powertrain Engineering Consortium (www.ricardo.comJdepe) 3 N Jackson, N Owen, M Wiseman and J Seabrook, 'Appropriate technology strategies for Hybrid vehicles - the key to successful products', 14th Aachen Colloquium, 2005 4 G G Lucas, Road Vehicle Performance: Methods ofMeasurement and Calculation, New York, Gordon and Breach Science Publishers, 1986 5 www.ricardo.comJdepe KEYWORDS Design of Experiments Emissions Engine calibration Fuel Economy Holistic Powertrain Hybrid Ratio optimisation Shift strategy Transmission calibration Global models © Ricardo UK Ltd

115

HYBRIDIZATION OF A SMALL SUV AND ITS CONTROL Baekbyun Cho, Nicholas D. Vaughan School of Engineering. Cranfield University This paper demonstrates the potential for fuel consumption reduction of a hybrid electric sport utility vehicle (SUV) using a quasi-static backward simulation model. The size for an electric machine (EM) was determined by the regenerative braking capacity, and the suggested value is 14kW for the 2200kg compact SUV. A rule based optimal control strategy of the charge sustaining hybrid based on the engine operating efficiency is proposed, and the influence of the control parameter on the fuel consumption and the final state of charge (SOC) is examined. The final simulation results over the standard driving cycles showed considerable fuel saving up to 23.0% with less than 2.2% SOC change.

NOTATION Aerodynamic drag area of vehicle

a VEH

Vehicle acceleration

C BAT

Battery capacity Aerodynamic drag coefficient of vehicle

Cd

J ENG

Rolling resistance of tyre Gravitational acceleration Battery output current Rotating inertia of electric machine Rotating inertia of engine

J FW

Rotating inertia of flywheel

J TMin

Rotating inertia of transmission input side

J TXout

Rotating inertia of transmission output side

J WHL

Rotating inertia of wheel

MVEH

Vehicle mass

PACC,ELEC

Electric accessory power

g iBAT

J EM

PACC.ENG

PBAT

Engine mechanical accessory power

PEM,Ioss

Battery output power Electric machine power loss

RBATint

Battery internal resistance

RFD

RTM

Final drive ratio Transmission ratio Wheel radius Battery state of charge Electric machine torque Engine torque

117

TTMin

Transmission input torque

TTMin,/OSS

Transmission loss torque at input side Wheel torque Open circuit voltage of battery Vehicle speed

Bgrad

Final drive efficiency Road gradient

Pair

Air density

'lFD

Electric machine speed Engine speed Transmission input speed Wheel speed

INTRODUCTION Hybrid electric vehicles (HEVs) have several advantages, as they offer an improvement of fuel economy and performance in the same package, along with the most significant advantage of absorbing kinetic energy through regenerative braking. Following the Toyota Prius, the availability of HEVs has recently expanded the application area from passenger cars to sports utility vehicles (SUVs) and pickup trucks. This paper demonstrates the procedure of the mild hybridization of a small SUV and a control parameter optimization. In this context, it is envisaged that an electrical machine (EM) is connected directly to the engine crankshaft and acts as an integrated starter alternator (ISA). In order to provide a comparison, vehicle models with both conventional and hybrid powertrains have been developed. In section 2, the quasi-static backward simulation model for the conventional baseline powertrain and its hybridized counterpart is presented. In section 3, the effect of the EM size on the recoverable energy from regenerative braking is examined. The control strategy with parameter tuning and fuel economy simulation result is explained in section 4, and are followed by the conclusions.

MATHEMATICAL MODEL Vehicle powertrain modelling techniques used in fuel economy studies can be divided into two categories according to the direction of the power flow calculation: backwardfacing, and forward-facing. While the forward simulation is more suitable for the study of the control strategy including driver's behaviour, the backward model is useful to reveal the maximum possible fuel economy potential. This study uses a quasi-static backward-facing simulation model which calculates the required power from the wheel to the engine. A mathematical model of the baseline conventional vehicle equipped with a continuously variable transmission (CVT) as well as the HEV was produced. All the fast dynamics of the sensors and actuators are assumed negligible, and only lumped rotating inertia is considered. For both vehicles, the required torque and speed at the transmission input can be calculated from Equations (1) ~ (4). The only difference is that the battery and power electronics mass should be added to the vehicle mass for the hybrid. However, the EM mass is not considered 118

because it is assumed that the EM is crankshaft mounted and is approximately the same as the flywheel which can be eliminated. VVEH OJWHL = - r WHL

TWHL

(1)

= J WHLOJWHL + rWHL {(M VEH + MBAT Xa VEH + gfroll + gsin Bgrad)+ ~CdPoirAdV~EH } (2)

OJ

OJWHL TMin-R R TM FD

(3)

(4)

The transmission ratio is an independent control variable and has a significant effect on the fuel economy. Most CVT control algorithms [1, 2] are based on the efficient operation of the engine at a given power. It is effective and easy to be implemented but not always true optimal in terms of the overall powertrain efficiency. The authors suggested [3] a control strategy using the ideal operating surface (lOS) with an operating map of the transmission ratio as a function of the required wheel torque and speed. This is optimal in terms of the tank-to-wheel efficiency, and adopted for this study. The required engine torque of the baseline conventional powertrain can be expressed by Equations (5) and (6).The mechanical accessory load of the engine was included in Equation (6). (5)

T ENG

= ( J ENG

.

+ J FW ) OJENG +

PACC,ENG OJ ENG

+ TTMin

(6)

In the case of the hybrid, there are 2 power sources to produce the torque required at the transmission input. Therefore, either of them could be a controlled variable, and the EM torque was chosen in this work. Equations (7) and (8) represent this relationship.

(7)

(8) The battery supplies the power to drive the EM and electric accessories. Using Equations (9) ~ (11), the battery output current and the state of charge (SOC) can be calculated from the total power extracted from the battery. In Equation (9), the power loss of the EM includes not only the mechanical loss but also the power electronics loss as a function of the EM speed and torque. Additionally, it is assumed that the mechanical accessories driven by the engine are converted to an electrical load on the 119

battery. The loss due to the internal resistance of the battery was included as seen in Equation (10). P BAT

=

TEM W EM

(9)

+ PEM.loss + PACC,ELEC

(10)

dSOC

iBAT

dt

CBAT

--=--

(11)

SIZING ENGINE AND ELECTRICAL MACHINE Engine Size The SUV application provides a good opportunity because of the relatively poor fuel economy in this segment. The authors have studied a generic compact hybrid SUV equipped with a spark ignition direct injection (SIDn engine and an infinitely variable transmission (NT), and demonstrated the considerable fuel consumption reduction [4]. In that study, the vehicle weight and the engine size were derived from the relationship between the vehicle kerb weight, engine peak power and the engine size, that is suggested in the two comparative studies [5, 6]. Using the same approach with the updated data of the 2006 model year SUVs in North American market, the vehicle kerb weight to engine peak power ratio and the specific engine power are revised as shown in Table 1. Applying these values to the compact SUV with 1590kg kerb weight, the appropriate engine size is 3.2L which is reduced from 3.3L in the previous work. [4] Table 1. Engine size 2006 model year Average kerb weight of compact SUVs 1590 kg 94.2 kW I 1000kg Engine peak power to kerb weight ratio Engine specific power 46.2 kW IL Appropriate engine size 3.2 L

2005 model year 1594 kg 93.2 kW I 1000kg 45.4 kW IL 3.3 L

Electrical Machine Size A systematic way of EM sizing [7] has been suggested but it is hard to generalise for the variety of applications of HEVs. In the earlier study of fuel economy [3], the authors claimed that the idle stop and the electrical accessory load driven by the regenerative braking energy occupied the majority of the fuel economy improvement in the mild hybrid. Consequently, the capacity of the regenerative braking is the most important factor of the mild hybrid design. The recoverable braking energy over a journey can be calculated by simulation for different EM powers. The speed profiles over the driving cycles were achieved with the CVT control strategy using the ideal operating surface [3]. Even though the EM power is given, the torque limit and the rotating inertia of the EM are variable design parameters. In this study, these parameters were scaled from the EM used in the Honda Accord hybrid. According to Kabasawa [8], the maximum power is 12 kW for motoring and 14kW for generating, and the maximum torque is +1- 136 Nm. The 120

rotating inertia of the rotor is assumed as 0.072 kg_m2 , which is the approximated value from the diameter, width and the average mass density of the rotor [9] as 240mm, 40mm, and 5500 kg/m2 respectively. The result for the 4 standard driving cycles is illustrated in Figure 1. The x-axis represents the maximum generating power capacity of the EM. The y-axis indicates the percentage of the absorbed mechanical energy by the regenerative braking with the specific size of the EM divided by the whole amount of vehicle kinetic energy in the braking phase. Generally speaking, a large EM can absorb more kinetic energy of the vehicle but may raise the space and the weight problem to install both the EM and battery. In the Japanese 10-15 mode, a 10 kW EM can capture 90% of the total available regenerative braking energy. In the Highway Fuel Economy Test (HWFET) cycle, it requires 19kW to store 80% of the kinetic energy. This quite different figure is caused by the vehicle operating conditions including the speed and the acceleration during the driving cycles. The 10-15 mode cycle has relatively low speed with mild acceleration but the HWFET represent high speed and dynamic driving conditions. The result of the New European Driving Cycle (NEDC) and Federal Test Procedure (FTP) -75 cycles lie between the two extreme cases. In this study, the appropriate level of absorbing energy is set as 80% in urban and 70% in highway driving cycles. To achieve this target, the required power of the EM in the generation mode is 14 kW, which is the same value of the reference EM being used in Honda Accord.

100 r 90

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10

---t----------1 ----

- a - FTP-75 HWFET

----A- 10-15

0

5

10

15

20

Regenerative braking power [kW]

Figure 1. Recoverable braking energy according to the EM power Figure 2 depicts the scatter plots of the EM operating points through the driving cycles with the 14kW EM generating limit represented as grey dotted lines. To capture more energy the EM torque has to be increased in the low speed region, but it is not very useful at high speed because most of the energy produced at high speed is distributed under 14kW.

121

NEDC

FTP-75

O.-r.r-.-.-.,r.,----~.r----,

E ~ Ql :J

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~ -200

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r5

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r5

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-300 1---------------+-------1 -400 I -500 '--~---'---~-----' 1000 2000 3000 4000 EM speed [rev/min]

Figure 2. Operating points for regenerative braking CONTROL STRATEGY AND SIMULATION Control strategy Many control algorithms have been suggested for REVs to find the optimal operating conditions. They are classified into 3 categories, as nonlinear optimal control [10, II], rule based intelligent control [12, 13], and predictive control [14, IS]. Even though the nonlinear optimal control and the predictive control show better performance than the rule based controller, it is harder for these to be implemented in real-time without additional information supplied from external devices. The authors have previously suggested a rule based approach based on the engine efficiency [3]. In this control strategy, the EM adds torque to the crankshaft to assist the engine when the required torque is over a certain value, or generate electric energy to charge the battery if the required torque is under a pre-defined level. Additionally, the assisting and charging torque level is controlled according to the battery soc. In this study, the control strategy is slightly modified by using the normalised engine efficiency. Figure 3 depicts the normalised engine efficiency contour. The thick black line represents the maximum torque of the engine. Using this information stored as a map and the current battery SOC, the controller determines the requested EM torque, as illustrated in Figure 4. The target nominal SOC is 60% and the normal operating limit is +1- 10%. As shown in the figure, the threshold of the normalised engine efficiency is the only parameter that can be tuned. The higher threshold value means that the EM works more actively to assist the engine or charge the battery. If the SOC is lower than 50%, assist is prohibited and the engine is operated up to its most efficient torque to charge the battery as much as possible. In contrast, the

122

EM does not charge the battery and is responsible for the portion of the required torque over the most efficient point of the engine when the SOC is over 70%.

1000

6000 Engine speed [rev/min]

Figure 3. Normalized engine efficiency ~ ~ c Q)

Engine+EM Max. torque

o

'u

~

Threshold

Q)

c '6> c Q)

100

'0 Q)

.~

co

E o

Z

Threshold

0 o

o

o

N

l.()

o

to

o

co

o o

Battery SOC [%]

Figure 4. Battery SOC control strategy Parameter optimization To decide the optimal threshold efficiency, backward simulation of the HEY is performed for the same driving cycles with 10% steps of the normalised engine efficiency. The fuel consumption and the final SOC are shown in Figure 5 by black and grey colours respectively. In all cycles, the trend is obvious, increasing the threshold increases the fuel consumption and the final SOC. For the fuel consumption, the 10-15 mode is the most sensitive, but there is no considerable change in the FTP-75 cycle. In 123

case of the final SOC, 10-15 and NEDC cycles show over 10% difference from maximum to minimum, but there is only 6% in FTP-75.

7o

9

---e--- NEDC .•( 6 8

-e-FTP-75 --HWFET -A--10-15

8. 8 8. 6

w~

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:[8 4

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o o

1

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8

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....... -4i ................,

::J

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.-',.,.

,~,~

6

54 52

o

I 20

10

30

40

50

60

70

80

90

5o

100

Threshold normalized engine efficiency [%]

Figure 5. Threshold engine efficiency optimization

The large difference of the SOC between the initial and the final implies the dependence of the fuel economy on the pre-stored electric energy. Therefore, it is preferable to maintain the difference as a minimum in the charge sustaining HEVs. To compromise this fact with the minimum fuel consumption, 60% of the normalised engine efficiency is selected as a threshold value for the controller. Fuel economy simulation

Parameter Ad C BAT

Unit [m2 ]

[C]

Cd

Table 2. Simulation parameters Value Parameter Unit J 2.68 [kg m2 ] EM 23400 [kg] MVEH 0.37 [kg] MBAT

0.01

PACC.ENG

[kW]

J TMin

[kg m2 ]

0.20

PACC.ELEC

[kW]

JTXOIlI

[kgm2 ]

0.20

RFD

J ENG

[kg m2 ]

0.16

rWHL

J FW

[kgm2]

0.10

17FD

froll

Value 0.072 2200 35.2 1.0 1.0 5.20

[m]

0.3677 0.98

The parameters used in the final simulation work are listed in Table 2. The parameters related to the battery are carried over from the Honda Insight [16] because the same 124

capacity of the battery is also used in the Accord. The efficiency maps of the engine, the IVT, and the EM, and the open circuit voltage and the internal resistance of the battery are scaled from the authors' previous study [4]. The final simulation results with this value are summarized in Table 3 and Table 4. The fuel consumption is considerably reduced from 2.4 to 23.0% with only -2.2 ~ + 1.3% SOC change. Considering that the baseline vehicle is already highly optimised with the combination of the SIDI engine and the IVT, this result shows good potential of the mild hybridization of the SUV.

Initial Final Change

Baseline Hybrid

Table 3. Battery SOC change Battery SOC [%] FTP-75 NEDC HWFET 60.0 60.0 60.0 59.2 61.3 57.8 -0.8 +1.3 -2.2

10-15 60.0 60.5 +0.5

Table 4. Fuel consumption Fuel consumption [LlI00km] (Improvement [%]) FTP-75 HWFET NEDC 9.55 7.49 9.56 7.31 8.19 8.21 (14.1 ) (2.4) (14.2)

10-15 10.50 8.09 (23.0)

CONCLUSIONS

Using the quasi-static backward simulation model, the fuel consumption reduction of the hybrid SUV was demonstrated. To hybridize the baseline vehicle, the EM power was determined by regenerative braking capacity, and the suggested value is 14kW for the 2200kg compact SUV. A rule based optimal control strategy of the charge sustaining HEV based on the engine operating efficiency was proposed, and the influence of the parameter on the fuel consumption and the final SOC was examined. The final simulation result over 4 standard driving cycles shows considerable fuel saving up to 23.0% with less than 2.2% SOC change. Future work will consider the impact of different road conditions in realistic driving cycles. ACKNOWLEDGMENT

Acknowledgment to Torotrak Development Ltd for use of a simulation package and data of the IVT REFERENCES

1. Yasuoka, M., et aI., An integrated control algorithm for an SI eninge and a CVT, in SAE International Congress and Exposition. 1999: Detroit, Michigan, USA. 2. Sakaguchi, S., E. Kimura, and K. Yamamoto, Development of an engine-CVT integrated control system, in SAE International Congress and Exposition. 1999: Detroit, Michigan, USA.

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3. Cho, B. and N.D. Vaughan, Dynamic Simulation Model of a Hybrid Powertrain and Controller using Co-simulation - Part II : Control strategy. International Journal of Automotive Technology, Submitted. 4. Cho, B. and N.D. Vaughan, Dynamic Simulation Model of a Hybrid Powertrain and Controller using Co-simulation - Part I : Powertrain Modelling. International Journal of Automotive Technology, Submitted. 5. Walters, 1., H. Husted, and K. Rajashekara, Comparative study of hybrid powertrain strategies, in SAE Future Transportation Technology Conference and Exposition. 2001: Costa Mesa, California, USA. 6. Husted, H.L., A comparative Study of the production applications of hybrid electric powertrains, in Future Transportation Technology Conference. 2003: Costa Mesa, California, USA. 7. Barnard, R.H. and C.M. Jefferson, Criteria for sizing the prime mover and energy storage capacity in hybrid vehicles, in 30th ISATA Conference on Electric and Hybrid Vehicles. 1997: Florence, Italy. p. 363-370. 8. Kabasawa, A. and K. Takahashi, Development of the IMA Motor for the V6 Hybrid Midsize Sedan, in SAE World Congress. 2005, Society of Automotive Engineers, Inc.: Detroit, Michigan, USA. 9. Miller, J.M., Propulsion systems for hybrid vehicles. 2004, London, UK: The Institution of Electrical Engineers. 10. Kleimaier, A. and D. Schroder, An approach for the online optimized control of a hybrid powertrain, in 7th International Workshop on Advanced Motion Control. 2002: Technische Univ. Munchen, Germany. p. 215-220. 11. Kirschbaum, F., M. Back, and M. Hart, Determination of the fuel-optimal trajectory for a vehicle along a known route, in 15th IFAC World Congress. 2002: Barcelona, Spain. 12. Johnson, V.H., K.B. Wipke, and D.J. Rausen, HEV control strategy fot real-time optimization of fuel economy and emissions, in Future Car Congress. 2000: Arlington, Virginia, USA. 13. Rajagopalan, A., et aI., Development of fuzzy logic and neural network control and advanced emissions modeling for parallel hybrid vehicles. 2003, National Renewable Energy Laboratory: Golden, Colorado, USA. 14. Rajagopalan, A. and G. Washington, Intelligent control of hybrid electric vehicles using GPS information, in Future Car Congress. 2002: Arlington, Virginia, USA. 15. Finkeldei, E. and M. Back, Implementing an MPC algorithm in a vehicle with a hybrid powertrain using telematics as a sensor for powertrain control, in IF AC Advances in Automotive Control. 2004, Elsevier Publications: Salerno, Italy. 16. Kelly, K.J., M. Mihalic, and M. Zolot, Battery usage and thermal performance of the Toyota Prius and Honda Insight for various chassis dynamometer test procedures. 2001, National Renewable Energy Laboratory: Long Beach, California, USA. © Cranfield University

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Concept and Potential of "CVT-Hybrid-Driveline" Prof. Dr.-lng. B.-R. Hahn Forschungsstelle for Zahnriider und Getriebebau, TU Munchen, Garching Dr.-lng. H. Pflaum Forschungsstellefor Zahnriider und Getriebebau, TU Munchen, Garching Dipl.-lng. D. Tomic Forschungsstellefor Zahnriider und Getriebebau, TU Munchen, Garching ABSTRACT

This report results in the transfer of the findings from the research project "Sonderforschungsbereich 365 - Environment-friendly Automotive Drive Engineering" to the succeeding project "Optimized Drivetrain" (Transferbereich 38 of the DFG). The latter is a close-to-production hybrid drivetrain being developed and designed in close collaboration with the industry. The main conceptual differences to the "Autarc Hybrid" (which emanated from SFB 365) are a simplified continuously variable transmission (the so called iJi transmission), the use of ultra-capacitors for energy and power storage instead of a heavy battery as well as an optimized total vehicle control system. The concept is presented and explained in this document. Results from simulating the performance of the drivetrain under varying operating conditions are reported. INTRODUCTION

Development in recent years shows the ever increasing relevance of hybrid driveline concepts for vehicles, especially in Japan and the U.S.A. These systems have the main intention to reduce fuel consumption. The increasing sales figures of hybrid vehicles currently available on the market, for example the Toyota Prius, substantiate the acceptance of this alternative drivetrain by the customer as well as the road performance. Forecasts predict that the world market share of hybrid vehicles by 2025 could add up to 50% [1]. The Technical University Munich has researched hybrid drivetrain concepts for passenger vehicles since 1993. The "Sonderforschungsbereich 365 - Environmentfriendly Drive Technology for Cars"(SFB 365) was created at that time with the aim to research a hybrid drivetrain concept and realize it in the form of a prototypical vehicle. The main elements of this powertrain are a continuously variable transmission (CVT) with a wide ratio range, a combustion engine, an electric motor with a nickel metal hydride (NiMH) battery as well as all associated and necessary components (e.g. controllers, hydraulics, etc.). An optimal design and layout of the components was done in such a way to maximize the overall efficiency of the drivetrain. Through this a significant fuel saving potential was identified and realized [2]. The individual components as well as complete systems were developed and their energy consumption was optimized by an interdisciplinary collaboration of six chairs of the TU Munich. The results of the testing of the mock-up on the dynamometer and in-vehicle testing showed promise for this concept. This was apparent especially during operation of the combustion engine and electric motor within efficient bands through recuperation of braking energy as well as through an intelligent operating strategy. Here remarkable savings were realized (see Figure 1). 127

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Figure 1 Fuel saving potential of the Autarc Hybrid in comparison to the reference vehicle (NEDC) However the figure shows also that the theoretically achievable fuel saving potential cannot be fully realized. This is due to the increased weight of the vehicle, lack of complete recuperation of braking energy and necessary compromises concerning the combustion engine operation (for drivability, long starting procedures). Small parasitic effects on some subsystems led to a less than optimal result in regards to fuel consumption [3]. Within the succeeding project "Transferbereich 38 - Optimized Drivetrain" the improvement potentials from the SFB were identified and are to be utilized. A transfer of the technology with its results into industry is to be realized in collaboration with the GM Powertrain Europe, ZF Sachs AG, ZF Friedrichshafen AG and EPCOS AG. Another important requirement for this technology to be transferable to the industry is to keep the overall costs comparable to those of current production vehicles. CONCEPT OF THE CVT-HYBRID POWERTRAIN Tests with the "Autarc Hybrid" (SFB 365) on the dynamometer and in the prototype vehicle showed that surplus weight and high electric energy consumption for hydraulics and auxiliary components nullified remarkable shares of the saving potential of hybrid concepts. Furthermore the complexity of the F-transmission with its extensive hydraulic system was criticized by the automotive industry. Starting from these insights and the requirements, the concept of the Autarc Hybrid was conceived. Research on the Autarc Hybrid has shown that the maximum overdrive transmission ratio of the F-transmission within drive operation and within norm cycles is only used rarely. Due to this the positive effects concerning fuel consumption are relatively small. By abandoning part of the transmission ratio range the complexity of the transmission can be noticeably reduced. The iJi -transmission is the result of this simplification. This transmission works with one shaft less than the i2-transmission, furthermore the new transmission can do with 2 clutches fewer than its predecessor (see Figure 2). The shifting procedure is carried out by just 2 synchronized clutches. This not only means

128

less manufacturing expenses and part costs, it also reduces the complexity of the hydraulic system. In addition to the reduction of drag and friction torques by having fewer clutches, it increases the mechanical efficiency of the transmission. Also the amount of oil leakage and therefore the hydraulic losses is reduced to a minimum. Overall it means reduced costs and weight, better driving performance and less fuel consumption. i~i-transmission

P-transmission

l2

K1

Figure 2 Comparison of concepts: il B i~i transmissions

Another important starting point in this concept is to go without a mlmmum electrical range. The Autarc Hybrid was designed to have an electrical range of 30 kIn. In the meantime, a noticeably improvement in emissions reduction has been developed, which has eased the emission problems in congested areas. Based on these boundary conditions, the heavy battery can be replaced by an ultra capacitor stack. An accumulator, which is slightly larger dimensionally, is on 12V-basis and serves as a charger for the ultracaps if they have a low charge. This reduces the surplus weight of the vehicle from 170 to 25 kg. Besides this noticeable weight reduction, which has an advantageous effect on fuel consumption and driving performance, the costs are also significantly lower as well as the increase in storage efficiency. Furthermore there is no need for a complex cooling mechanism and battery management including auxiliary electrical consumption for the controller and fan because the efficiency of the ultra cap stack is more efficient than a conventional battery. Practical tests with the Autarc Hybrid have shown that the starting procedure of the combustion engine with a conventional starter has less than optimal. By the implementation of a kick start principle which uses the inertia of the electric motor and the transmission for acceleration of the combustion engine by the principle of a fast shifting procedure the starter can be abandoned. An optimized starting procedure concerning fuel and electric energy consumption is to be introduced (see Figure 2). The optimized CVT-Hybrid-Driveline

The elementary conception of the Autarc Hybrid as a parallel-hybrid is retained. The driveline combines again a small efficient electric motor with a series production combustion engine (see Figure 3). 129

D motor

Figure 3 The CVT-driveline The core piece of the new hybrid vehicle is the continuously variable iJi transmission which is based on a CVT chain converter such as the one in the i2 transmission. It uses the ratio range of the chain converter twice through a shifting mechanism. Figure 4 shows schematically the configuration of the iJi transmission in mode VI (low velocity mode) and V2 (high velocity mode). In contrast to the F transmission the electric motor and the combustion engine are not placed on one shaft but instead located on two opposite shafts of the CVT (see Figure 4: WI, W2). The combustion engine is engaged by a clutch LK on shaft WI. In mode VI the clutch KI is closed whereby K2 is opened. In this mode the electric motor powers W2 and through the CVT WI while the transmission can be adjusted continuously variable. At low driving speed where an optimal pull-away and recuperation can be realized whereas the combustion engine is not in operation in this mode normally. V2

VI

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iJi -transmission

Depending on the power demand of the driver, the combustion engine is engaged and started at driving speeds between 7 and 20 kmIh. This shift from mode VI to V2 occurs without torque interruption. In mode V2 (see Figure 4, right) the clutch K2 is closed and Kl is open. The electric motor is then coupled directly to the output over gear i23 . The combustion engine is at that point able to operate over WI and the CVT on W2 while the transmission can be adjusted continuously variable. While in mode V2 it is possible to operate the

130

combustion engine with a high efficiency. The electric motor can also, if necessary, work as a generator and charge ultracaps and battery or supply the on-board electrical system. Also it can be used to support the combustion engine by boosting. As the storage capacity of the ultracaps is limited, they are mainly charged during recuperation phases. The starting procedure of the combustion engine is induced by engaging the multiple disc clutch LK and by that it is connected to the turning shaft WI. To assure that this procedure is imperceptible for the driver a traction force loss has to be avoided. Therefore it is necessary to provide additional power from the drivetrain which is realized by a fast adjustment of the CVT so that the flywheel mass of electric motor and shaft W2 is decelerated. As far as possible the electric motor continues to power. By this CVT-supported starting procedure it is possible to realize very short starting durations. The concept of the electrical system is shown in Figure 5.

12V battery

Figure 5 The electric system For safety reasons the size of the ultra cap stack is designed to make at least 3 pullaway and starting procedures possible without having to recharge them. The ultracaps have to be then recharged by the battery. The capacity of the ultracaps has to be sufficient for a full energy recuperation when breaking from 50 to 0 kmIh. To be able to provide an electric start even after a long standing time of the vehicle, it is necessary to charge the ultracaps over a DC -DC conversion from the 12 volt battery (see Figure 5). The battery also feeds the onboard electrical system as well as the auxiliary components which are necessary for the controllers, pumps etc. When the combustion engine is running the electric motor can be used as a generator. The controller layout used in the SFB 365 is enhanced with the aim to reduce electrical power consumption as well as costs. The controller layout is described in Figure 6. Possibilities to combine functions of each controller subsystem are being developed. The operating strategy which was developed and verified for the Autarc Hybrid is being modified, customized and extended for the new drive train. Especially the limited ultracaps storage capacity and the different controllability of combustion engine and electric motor in the modes VI and V2 must to receive particular attention.

131

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!

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Figure 6 Controller structure for the CVT-Hybrid driveline The prototype vehicle is based on a current Opel Vectra. The following table shows the main data of the CVT-Hybrid driveline.

Table 1 Benchmark figures of the technical data of the CVT-Hybrid driveline Vehicle Basis Vehicle Curb weight of production vehicle Maximum permissible weight

Opel Vectra 1556 kg 1995 kg

Engine Engine Marking Maximum torque Maximum power

Diesel direct injection Z19DT 280 Nm /2000 - 2750 min-' 88 kW /3500 - 4000 min-'

iJi Transimission Ratio Range of CVT

iJi

Ratio range of -transmission Overdrive Start transmission

6 14.3 1.83 26.3

Electric Motor Electric Motor Maximum power Nominal speed Operational voltage

Permanent magnet synchronous machine 14kW 1500 min-' 26 ... 52 V

Energy storage Storage type Capacity

Ultra capacitors 225 Farad - usable 220 kJ

132

THE OPERATING STRATEGY The development of an intelligent operating strategy for this hybrid concept is crucial for ensuring the functionality of the power train. This will ensure maximizing fuel saving potential in any driving situation and system state.

Starting procedure The basic principle concerning the starting procedure has been described in the operating strategy. In principle the vehicle pull-away by the electric motor if system state and boundary conditions allow it. In all other cases, special strategies come into operation (i.e. low ultra cap charge, start on a hill, etc.). At the beginning the power train is in start-up position, i.e. the transmission is in mode VI and the CVT provides the maximum ratio for the electric motor. Depending on the power demand of the driver the electric motor provides the torque for acceleration of the vehicle. This procedure is described in Figure 7 by highlighting the power flow through the drive train. LK

Figure 7 Electromotive start in VI Clutch KI is engaged and the electric motor is powering over CVT and shafts WI and W3. The gear ratio ofthe CVT is kept constant until shaft W2 achieves a sufficient speed. The kinetic energy of the rotor, shaft and variator discs has to be high enough to compensate the drag toque of the combustion engine when it is coupled and the fast adjustment of the CVT is started by which the flywheel masses are decelerated is executed. By this mechanism there is no traction force loss at the output and the combustion engine is started unnoticed to the driver (see Figure 8).

133

LK

Figure 8 Accelerating the combustion engine by quick ratio change of the CVT

As soon as the combustion engine has reached a minimum speed it is started and it begins to power the drive train. After the CVT has adjusted the UD ratio by which synchronization for a shift into mode V2 is established clutch K2 closes while Kl opens simultaneously; the ratio of gears il3 and b3 is designed that way that there is no differential speed in both clutches Kl and K2 for UD ratio of the CVT (see Figure 9). In mode V2 the whole ratio range of the CVT is available again for the combustion engine. LK

Figure 9 Drive by combustion engine in V2 Driving with constant speed in V2

In mode V2 the optimal operation point of the drive train depending on the velocity of the vehicle is adjusted by the CVT. The main aim is to operate the combustion engine and the drivetrain at a point at which the overall efficiency is the highest. Accelerating in V2

Depending on the power demand of the driver, speed and torque of the combustion engine are adjusted by the CVT. This operation is carried out so that the operation point stays at the optimal point which is minimum current fuel consumption. For example if 134

the driver demands maximum power, the CVT adjusts the speed of the combustion engine into this point. When the vehicle starts accelerating the CVT adjusts its ratio continuously so that the combustion engine stays in this point until the driver stops his power demand. In the case of kick-down the electric motor can boost additionally. The drive train is then able to provide a maximum power of Pcombustion engine + P electric motor = 102 kW.

Decelerating in V2 When changing from drive mode to coast mode by releasing the throttle the combustion engine is disengaged and turned off to save fuel. The electric motor starts generating a drag torque simulating the drag of the combustion engine. The generated energy is saved by the ultracaps. If the driver starts braking additional recuperation power from the electric motor is generated up to a maximum while the mechanical brakes also come into operation if necessary. To drag along the combustion engine without shock, if the driver wants to start accelerating again, the electric motor switches into drive mode again. If the velocity of the vehicle falls below 10 kmlh, the transmission shifts back into mode VI. If the ultracaps are not fully charged, the recuperation continues in VI.

Pull-away on a hill The pull-away.strategy described before is not applicable for a starting procedure on a hill if the slope exceeds a certain limit. The main reason is that in the given configuration the power ofthe electric motor is not high enough to start a pull-away and drag along the combustion engine simultaneously. For an uphill gradient higher than 12 % another pull-away strategy is applied although theoretically an uphill gradient of up to 20 % can be achieved with the strategy explained above. This alternative strategy is combustion engine-powered pull-away. For this the combustion engine is dragged along by the electric motor while the drive train is open, i.e. the clutches Kl and K2 are uncoupled (see Figure 10). LK

W3 K1

~~Ht--~I=-i:8J'-::

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Figure 10 Start ofthe combustion engine within the open drive train As soon as the combustion engine is started clutch LK is disengaged and Kl is closed (mode VI). By a controlled engagement of LK the vehicle is accelerated. To

135

keep the thermal load of the dutch low the electric motor supports this acceleration procedure (see Figure 11). LK

Figure 11 Start strategy uphill By the given configuration of the drive train a slope of up to 30% at fully loaded vehicle can be accomplished. Depending on the equipment the uphill grade can be detected by a sensor. Alternatively the driver can set the uphill mode manually. Strategy for uncharged ultracaps If through disadvantageous conditions, for example stop-and-go traffic, the charge of the ultracaps falls below a limit and a standard pull-away is not possible, the remaining energy in the ultracaps is used to start the combustion engine while the drive train is open, i.e. the clutches K1 and K2 are disengaged (see Figure 10). As soon as the combustion engine is started the electric motor starts working as a generator and charges the ultracaps. RESULTS FROM SIMULATION A complete simulation model of the drive train was built up within the simulation software ITI-Sim. This made it possible to research the behaviour of the drive train in all simulated situations, to optimize the operating strategy within the conception stage and to support construction and design. Under the given boundary conditions a pull-away on a plane ground with a power demand of 100 % shows the following behaviour of the drivetrain (see Figure 12).

136

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400

300

200

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Figure 12 Start 100% throttle Figure 12 shows output torque (T), vehicle velocity (v), speed of the combustion engine (n) and ratio of CVT (i). Additionally the input torque of both engines is shown in the lower diagram. As can be derived out of the diagrams the electric motor powers the drive train at pull-away. After t "'" 1.4 s the electric motor reaches its field weakening range wherefore its torque decreases along the power hyperbola. At t "'" 1.8 s the combustion engine is getting coupled in and dragged along. Also the quick adjustment of the CVT is lead in to get an additional torque from the deceleration of the electric motor and shaft W2 (JdJ). At t "'" 2.2 s the combustion engine is started and powers the drive train while the electric motor turns off. For description of an uphill pull-away the following diagram shows the behaviour of the drive train in the extreme case of having an uphill gradient of 30% and fully loaded (see Figure 12). At the beginning the combustion engine is already on and starts powering the drive train immediately. The electric motor supports the combustion engine as described above. Figure 13 displays the speed of the combustion engine in comparison to the speed of shaft WI as well as the velocity of the vehicle (v).

137

3000

60

2500

50

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~

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)/

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After t "" 2.7 s the speed of the combustion engine and the speed of shaft WI are equal and the clutch is closed. The thermal load of the clutch until this point adds up to 1.4 J/mm2 and is unproblematic even under these extreme conditions. After 2.8 s the acceleration of the vehicle declines. The reason is that after the clutch LK closes the electric motor boost stops. This is necessary to keep enough energy in the ultracaps for eventually upcoming new starts. CONCLUSION

The described concept of the "CVT-Hybrid-Driveline" is a further development of the hybrid drivetrain (i2 transmission) from the so called "Sonderforschungsbereich 365" under consideration of changed requirements. Simplification of the transmission was a basic requirement for a transfer of the project's results into industry. Simulations show that this simplification and its consequences on the transmission's ratio range does not mean higher fuel consumption under operating conditions. Reduction of vehicle weight through substitution of a heavy battery by ultracaps even increases the fuel saving potential. First results from simulation show fuel consumption of the current concept of 5.52 111 OOkm within NEDC. This also means the fuel saving potential compared to the adequate series-production vehicle which is equipped with a conventional manual transmission adds up to over 10%. It is shown that the very wide ratio range of the Autarc Hybrid transmission in practice has no major effect on fuel consumption as overdrive operation points are rare. Another advantage of the new concept is the optimized operation strategy and controller layout. Results from the simulation also prove full functionality even under extreme conditions as well as the dynamics of the CVT-Hybrid-Driveline. LITERATURE 1 D Schroder, 'Hybrid und Diesel favorisiert', Automobilwoche 7, 2004, p.15. 2 B R Hohn and B Pinnekamp, 'The Autarc Hybrid: A Universal Power Train Concept for Passenger Cars', International Gearing Conference, Newcastle Upon Tyne, England, 7.-9. September1994.

138

3 B R H6hn, H Pflaum, P Guttenberg, 'Experiences of the Autarc Hybrid Drive Line on Test Rig', EVS 18, Berlin, Germany, 20-24. October 2001, Proceedings, p. 217.

ACKNOWLEDGEMENT The authors would like to thank the DFG (Deutsche Forschungsgemeinschaft) as well as the participating companies EPCOS AG, GM Powertrain Europe, ZF Friedrichshafen AG and ZF SACHS AG for their financial and professional support.

139

Power combining single regime transmissions for automotive vehicles Frank Moeller NexxtDrive Ltd (www.nexxtdrive.com)

INTRODUCTION

There is a high level of interest in hybrid transmission designs that combine inputs from an internal combustion engine and one or more electric motors, despite the fact that the high cost premium of current production units cannot be rationally justified by the relatively modest consumption improvements. But there are new transmission designs which can offer significant further improvements in fuel economy and emissions reduction, together with vehicle performance benefits, at a cost comparable to a conventional automatic transmission. The field of automotive vehicle main drive transmissions includes applications from 250 W to 1000 kW, to suit light electric vehicles, mopeds, scooters, motorcycles, cars, trucks, buses and off-highway vehicles. This paper discusses four types of 'Power Combining Technology' (PCT) which will be key enablers for the hybrid operation of such vehicles.

2

3 4

The 3 branch electric powersplit transmission directly mounted to the engine, as pioneered by Toyota; The 3 branch electric PCT with 1 motor/generator contained in each drive hub, the second motor/generator and a brake being coupled directly to the prime mover as proposed by NexxtDrive; The 3 branch electric PCT with 2 motor/generators contained in each drive hub of small 2, 3 and 4 wheelers as proposed by NexxtDrive; The engine-mounted efficient 4 branch electric PCT as proposed by NexxtDrive under the name of "DualDrive".

These transmissions are suitable for hybrid and non-hybrid vehicles. No clutches or couplings are required in the drive train and the integrated electrical machines can drive the vehicles purely electrically and can also act as starter motors and battery charging generators. They all are fully automatic, stepless and can make the vehicles considerably more efficient than any conventional drivetrains. The paper will classify the various applications, show detailed layouts and explain their function and features.

141

1

POWER COMBINING TECHNOLOGIES

Power Combining Technologies (PCT) are transmission designs which make use of both a mechanical power source (usually from an internal combustion engine) and an electrical power source in combination with electric motor! generators. The drive systems can include electrical energy storage, as in the many 'hybrid' vehicle designs on the market or under development today, or the transmissions can be entirely selfcontained. Such transmissions are fully automatic, stepless and highly efficient compared with conventional vehicle drivetrains. They can eliminate the need for a variety of conventional transmission components including alternators, starter motors and clutches. Opportunities for PCT reach far beyond automotive primary powertrain applications, to include PTOs and supercharger drives, electric bicycles, locomotives, ships and wind turbines. The PCTs under discussion here make use of epicyclic gear trains to combine the inputs from engine and electric motors. The three branch epicyclic transmission developed by Toyota - the 'Powersplit' drive - is well known and will only be used for reference here. Three other drivetrain configurations will be discussed, however, each of which offers significant advantages in specific vehicle applications. 1.1

Efficiency

Continuously variable transmissions (CVTs) theoretically allow engines to operate at their most efficient speed for any given power output. Unfortunately the durability of mechanical CVTs is reduced because their systems rely on friction between two surfaces moving at slightly different speeds. PCT systems use efficient epicyclic gears to transmit their power and are combined with two motor ! generators. Generating electricity in one place and using it to power a nearby motor is relatively inefficient, around 80 percent compared with 98 percent for a typical epicyclic gear train, but the best PCT designs ensure that a low percentage of the total power is transferred via the electrical path. This results in much higher overall efficiencies. 2

SINGLE MOTOR HUB TRANSMISSION

The space within the hub of a vehicle is generally largely empty and it would be highly desirable if this space could be used to accommodate the power combining device of a hybrid vehicle. This would comprise a 3 branch epicyclic gearset with a sun gear, a set of planets on a carrier arm and an outer ring gear, along with a single reversible machine.

142

eltctriClllnpUl

lhcII..lcaIlnpUl

Figure 1 Hub with externally mounted motor / generator The machine in the hub could be a hydraulic motor, but is more likely to be an electric machine for ease of control and easy integration with electric-hybrid schemes. The machine might be mounted externally to the hub (figure I), or fully integrated within it. The system would be constructed so that the sun gear rotates with the input from the vehicle's engine, while the electric motor powers either the planet carrier or the ring gear, depending on the application, with the hub body connected to the other member. By altering the speed and power absorbed or output by the electric motor, the effective transmission ratio between the input shaft and the hub will be altered, giving full, independent control over both speed and torque at the vehicle wheel.

2.1

Vehicle weight implications

The hubs of a vehicle are basically unsprung. It is undesirable to increase the unsprung weight of a vehicle because this severely impairs the road handling characteristics and driveability of the vehicle. To minimise unsprung weight, the gearset can be designed so that the motor operates at high speed, allowing it to be small and light. Additionally, as the electric motor in the transmission is reversible and can also absorb power, the motor can serve as a brake, eliminating the need for a heavy separate mechanical brake in each wheel. Energy absorbed by the motors during braking can be fed to the vehicle's battery or storage capacitors, delivering fuel economy benefits through reuse to aid acceleration later. In emergency stop situations, however, it is likely that the electrical system's ability to absorb energy will be overwhelmed. By installing a single central mechanical brake, connected to the vehicle's driveshaft close to the engine, sufficient power for emergency braking can be delivered without adding unsprung weight (figure 2, figure 3). Distribution of this braking force can be controlled via inputs to the hub motors, allowing vehicle stability to be maintained. 143

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Single Matur Wheel Hulls

Transfer Box

Figure 2 Four wheel drive arrangement with single motor hubs In use in a vehicle, electrical power for the hub motor or motors would be provided by a second reversible electric machine coupled directly to the engine output. This machine could also replace the vehicle's starter and alternator. As the final gearing is provided in the hubs, the vehicle's driveshafts can rotate faster than is conventional; allowing them to be of lighter construction, with smaller associated gears. As the drive system allows independent control of speed and torque at the hub, it eliminates the need for mechanical clutches when the vehicle is stationary. Together, these effects will allow overall vehicle weight to be reduced substantially, with commensurate benefits in efficiency, performance and economy. (Battery)

Engine Motor I Generator Single Motor Wheel Hubs

Figure 3 Complete 2 wheel drive axle with concentric engine, motor generator, brake and 2 single motor hubs in wheels

144

Figure 4 Two motor hub for light electric vehicles and micro-hybrid vehicles

In a configuration with multiple driven wheels, each containing such a motor and gearset combination, the speed and torque at each wheel can be controlled independently, eliminating the need for one or more differential gearsets or LSD's, allowing the vehicle designer precise control over power distribution to manage wheel slip and stability in demanding conditions.

3

TWO MOTOR HUB

In this design, rather than relying on electrical energy generated by a second machine external to the hub, two electrical machines can be integrated with the epicyclic gearset within the hub body itself (figure 4). In use, one of the motor/generators generally acts as a generator and transmits electrical power to the other motor/generator, which acts as motor. The amount of electrical power so transmitted may be selectively varied by means of the controller, also altering the speed ratio of the system. Power is transmitted through the system both mechanically and electrically, in proportions which vary with ratio and electrical input from the energy storage device. 4

DUALDRIVE

While all the preceding transmission configurations were three branch designs, it is possible to add an additional degree of freedom to a system with two electric machines, allowing them both to be controlled independently of the rotation of the mechanical input and output. The DualDrive system has been designed as an automotive transmission system that operates on this principle. The epicyclic gear train used in the DualDrive system is unusual in that it has no outer ring gear. Instead, three sets of planet gears are mounted on a single planet carrier. Each set of planet gears engages with its own central sun gear. All the planet gear sets rotate on common shafts and are keyed to those shafts so that they must rotate at the same speed. The first and third planets sets mesh directly with their respective suns. The second set of planets meshes with a set of idler gears, also mounted on the planet carrier, that reverse its direction of rotation with respect to its sun (figure 5).

145

Figure 5 The DualDrive operating concept

The vehicle's engine rotates the planet carrier. The first, 'high torque' motor / generator drives the sun of the gear set 1, which in turn drives the first set of planet gears. The second, 'high speed' motor / generator drives the sun of gear set 2, which in tum drives the middle set of planet gears, via the idler gears on the planet carrier. The vehicle's driveshaft is connected to the sun of gear set 3, which is driven by the third and final set of planet gears. The high torque motor / generator can be used to generate power to drive the high speed motor / generator (and vice-versa). The combined motions of the planet carrier (defined by engine speed) and the planets (defined by the sum of the torques from the two electric machines) define the speed of the vehicle's driveshaft. If neither electric motor is transmitting any torque, the engine is free to idle without turning the wheels - obviating the need for a clutch. Different gear ratios are achieved by altering the relative torques applied by the two motor generators. By holding one or other of the electric machines stationary and allowing the other to spin freely, two separate gear ratios are achieved when the system is operating at the full efficiency of the mechanical gearbox. Between these fixed points, electric inputs are minimised - less than half the amount of electric power rating is required compared to "three branch" epicyclic transmissions. This means that the electric machines are extremely compact and low in cost. For hybrid operation, it also means a smaller, lighter, cheaper and more compact battery can be used. There are two ratios at which the electrical path transmits no electric power. The system is sized so that these ratios occur at common operating speeds. In this way, overall efficiency is maximised.

146

Figure 6 The DualDrive 4 branch transmission 4.1

Compact packaging

Packaging considerations are critical for any automotive transmission design. DualDrive is designed to fit into the volume of a conventional manual transmission. The compact nature of the epicyclic gear train and the absence of clutches or torque converters provide space for the two electric motors. While their power outputs are the same, the torque requirements for the system's two motor! generators are quite different. As torque defines motor size, this allows the second motor generator to be positioned concentrically inside the first, reducing the overall size of the device (figure 6). The absence of ring gears also reduces the size of the mechanical element of the transmission, as well as reducing overall cost by eliminating these large, high-precision components. Overall, the small size of the DualDrive system allows it to be substituted directly for a conventional transmission in many applications. Alternative configurations will even allow its use in applications where packaging constraints preclude the use of a conventional hydraulic automatic gearbox. Two such configurations are shown below (figure 7, figure 8)

147

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Figure 7 DualDrive configured as a front wheel drive transmission

Figure 8 A compact 2 wheel drive axle with transverse engine and, 2 DualDrive transmissions including concentric final drives, overall length = 600 mm The compact size of the DuaIDrive system offers designers additional flexibility in drivetrain design for larger vehicles. Multiple DuaIDrive systems may be installed in the drivetrain of large all-wheel drive vehicles, for example, allowing full control of speed and torque at each wheel. Such a configuration is shown below (figure 9).

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Figure 9 A DualDrive configuration for a large Hybrid or non Hybrid SUV or off road vehicle, allowing independent control of driven wheels 4.2 Power requirements Because the electrical load path only transmits a fraction of the power, in the base case of a 100kW engine for a C-class vehicle for instance, the electric machines in the DualDrive system would need to be rated at 26.3kW max - less than half the electrical power requirements of a three branch transmission. The relatively small electrical contribution is an important factor in delivering the system's high overall efficiency. 70

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Reliability

Like any well-designed electric motor and gear combination, DualDrive will deliver an extremely long service life. The absence of clutches and friction materials in the device eliminates a key set of wear parts and also means that the need for transmission oil changes is removed, since the oil is not contaminated by wear particles.

4.4

C02 reduction

Simulations demonstrate that DualDrive could reduce vehicle C02 output by up to 20 percent in conventional drive trains and at least 35 percent as part of a hybrid configuration. In addition to the benefits provided by hybridisation, these reductions will be achieved by a combination of factors. Like all CVT approaches, DualDrive allows the vehicle's engine to operate at its most efficient speed for any given power requirement. But DualDrive provides high overall efficiency - compared with alternative CVT solutions, DualDrive is extremely efficient, minimising C02 generated to overcome transmission losses The DualDrive system can be used to provide a stop-start capability, shutting off the engine when the vehicle is stationary or coasting, restarting it in fractions of a second when power is required. This can deliver more emissions and fuel savings in everyday use, and can be applied to non-hybrid applications of the system. Because DualDrive always allows the engine to operate at its most efficient speed, further CO 2 benefits can be obtained through engine downsizing.

5

CONCLUSION

Power Combining Transmissions offer a wide range of benefits for vehicle designers. The uptake of these technologies is aided by recent advances in electric motor, power electronics, super capacitor and battery technology. Interest in their use is being driven by legislative and consumer pressure on vehicle manufacturers to improve efficiency and reduce emissions. New opportunities to exploit such technologies are emerging, thanks to demand for new classes of lightweight, efficient vehicles to fill specific niches in the transportation infrastructure of the industrialised world and to assist in the provision of mass mobility in developing nations. It is expected that these drivers will lead to mass production applications of the technologies discussed in this paper within a very short time. © NexxtDrive Ltd.

150

DRIVEABILITY VALIDATION USING MAHLE POWERTRAIN'S IDAA TOOLSET D Baker, T Girling, G Kennedy, D Pates, and B Porter MAHLE Powertrain Ltd., UK ABSTRACT

Validation of complex systems is a constant problem for vehicle manufacturers. The key to eliminating unacceptable behaviour is in the way the engineering experience is captured and incorporated into the validation test process. The developer must be able to look at the 'right' things in the 'right' way - and ideally as many of these as possible. Recognising the problems and shortcomings of available tools for assessing real world operation, MAHLE Powertrain Ltd developed its Integrated Data Acquisition and Analysis software toolset (IDAA), with the aim of maximising the effectiveness of validation data collected; maximising function coverage against channels logged; combined with powerful analysis capability. Originally developed to assist in the validation of robust On Board Diagnostics (OBD) systems, IDAA effectively enabled engineers to take control with only minimal additional resource and no need for specialist resource (eg. MatIab specialists). Use of the tools enabled engineers to take an integrated approach to the typically fragmented tasks of logging, data processing and reporting. This paper reviews the recent application of IDAA to validating driveability. Using quantifiable Measures of Success established from vehicle development experience, IDAA has been used to effectively assess and report on key driveability areas often associated with customer complaints, including starting, idle control and transient response. IDAA has not only been effectively used to validate mainstream programmes; it has also been successfully applied to programmes destined for emerging markets including China and South America where environmental conditions, customer usage patterns, and fuels can also affect driveability performance.

153

INTRODUCTION Background to driveability and driveability assessment

Driveability has historically been one of the most subjective parts of the calibration process and is very difficult to consistently develop, appraise and validate. The basic principle of driveability is that of a subjective feeling in many cases and much work has gone into developing these subjective ratings into more objective and repeatable results. At a basic level some aspects of driveability are associated with the functionality of the vehicle - starting, idle control, smooth response to throttle changes, e.t.c, whilst at a more detailed level they help define the characteristics of the vehicle, often referred to as the vehicle's 'DNA'. Typically manufacturers will identify various high level/subjective and low level/objective driveability targets for an application and will have their own assessment and testing procedures to measure their performance against these. Subjective ratings, typically using a scale of one to ten, are used to assess key areas of driveability for which it would be difficult to assign and assess objectively. The assessment criteria, or Measures of Success (MoS), will have been developed by teams of experienced calibration engineers in order to characterise some of the main areas of driveability: • Start Constant Load (road load and crowds) • • In Vehicle Transient (including open/closing throttle, fuel cut) • Idle • Speed Limiting • Transmission Shifts The aim of driveability development and validation is to ensure that the driveability responses of the powertrain system are robust to driver inputs, environmental conditions, market fuels, drive styles and driver 'abuses' that the vehicle is likely to encounter. To assess and validate the driveability calibration three methods are typically used: 1. Structured test track based driver assessments A prescribed set of tests is carried out to cover the operating envelope of the vehicle. The use of a structured and controlled test approach improves the accuracy and repeatability (and hence objectivity) of the subjective assessments. Both subjective and objective data may be collected for each test condition. Typically the objective data will only be used to assess some specific driveability measures, e.g. start times, and in the investigation of any issues identified from the subjective data. 2. Subjective driver assessments and feedback Subjective ratings are recorded for different operating conditions that replicate real world use as closely as possible. The rating data and comments can be recorded in a database that allows reporting and analysis of the results. 3. Validation testing performed in territories A mixture of structured and unstructured testing will be carried out in territory with the aim of assessing driveability performance under a range of extreme or limit market conditions. Data can be processed locally or sent back to the

154

engineering centre for processing and analysis as part of the development programme.

Problems with conventional driveability assessment approaches These assessment approaches go some way towards providing the clear understanding of the powertrain's response to the range of customer and market conditions that is required. But they fall short in a number of areas: • Reliance on subjective assessments • Limits accuracy • Comparisons difficult • Key opinion leaders can influence ratings • Detailed track assessments are very limited • Assessments carried out infrequently • Only in prevailing conditions • Many conditions not tested in detail • Limited use of objective data in assessment criteria • Subjective assessments can be broken down into objective measures • Potential data collection opportunities not realised • Objective measures can be applied to any recorded data • If all vehicle drives were logged then there would be a vast resource of driveability data Ideally the driveability engineer should have the capability to analyse any response of the system across any vehicle at any location in order to assess the performance. Additionally the engineer should also be able to assess his data coverage, i.e. which conditions haven't been tested. Until now there has not been a practical solution available to the engineer to provide this capability.

Application of integrated data acquisition and analysis (lDAA) To respond to this requirement, the data handling and analysis toolset for the acquisition and automated analysis of data, known as Integrated Data Acquisition and Analysis (IDAA), was developed. This toolset has been successfully implemented on a number of calibration features predominantly for On Board Diagnostics (OBD) development and validation. Through the use of automated high-resolution data loggers it is now possible to gain real world high resolution data from vehicles operating at locations throughout the world. Processing the resulting large quantity of data and presenting the data in the ways that will be of use and of interest to the engineer requires an automated and intelligent processing approach.

155

IDAA approach IDAA automates the traditional data analysis approach, allowing engineers to focus in greater detail on the real issues. Complex datasets from multiple engines/vehicles can be automatically processed, analysed and easy to understand reports generated. The automatic processing is supported by configuration tools, which are simple for any engineer to use. Using the configuration tools the engineer will define the way the data is to be processed and the way the processed data should be presented. An overview of the toolset is shown in Figure 1.

Reports

Applications experience Quality Guidelines Performance Targets Previous I Library

Figure 1: System overview diagram The module responsible for the analysis contains a script called a Data Specification Document (DSD) which is a self contained set of processing instructions together with information on the logging channels to extract from the datalog and the output signals required to produce a report. To effectively analyse the recorded data the DSD is defined using previous engineering experience of the event being assessed and an understanding of the objective measures which characterise that event. The DSD analysis capability supports complex processing approaches - the actual processing is performed using a 'Matlab' processing engine. The DSD analysis can be set up with appropriate trigger conditions, e.g. engine start analysis is performed at start, or trigger repeatably throughout the datalog for examples such as returning to idle and tip-in analysis. The development of the DSD defines which Engine Management System (EMS) signals are to be recorded and their acquisition rate. To prove the DSD's robustness and validate any modifications or changes to the IDAA toolset, a library of datalogs with known characteristics is used. These have been processed using the DSD and then correlated against a manual assessment of the same data using traditional techniques. The IDAA reporting tool provides several formats for viewing the data, the most common being the trend and distribution views, see figure 2. Trend views provide the traditional XY scatter plot and can support several channels of data with upper and

156

lower thresholds on a single plot. Distribution views allow the spread of data to be presented for the operating region, again with upper and lower thresholds displayed. In addition, distribution views have the ability to provide a statistical analysis of the spread of data against the limit value(s), giving a confidence measure for that data.

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Figure 2: Example data presentation OBJECTIVE This paper demonstrates the application of the IDAA toolset to vehicle driveability validation. It covers the processing of data using driveability specific script files, analysing this data against defined measures, and then generating reports from generic templates that allow driveability perfonnance to be clearly assessed. ENGINE START ANALYSIS

Measures of success for engine start Figure 3 highlights some features of engine starts from different example engine speed profiles. • Start time - battery voltage drop to engine speed reaching target idle speed • Run-up rate - average gradient of engine speed from crank to target idle speed • Number of stumbles - rate of engine speed becomes negative • Flare speed - maximum engine speed reached above target idle speed

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Figure 3: Example engine speed profiles Definition of DSD - analysis scripting This start DSD includes a number of user definable thresholds to allow modification to the detection of specific conditions. These have initially been set using engineering judgment for the demonstration vehicle but may be easily modified for other applications. From the raw data inputs a number of further channels are calculated and a number of predefined conditions identified. These conditions divide the start into six main sections, pre-crank, cranking, run-up, flare, post-flare and stable idle. These are then broken down into further subsections associated with each specific MoS. Additional calculations are performed to identify if any external or driver induced conditions occur, which could affect the measured start characteristics. For example, on a manual vehicle if the driver was to engage a gear and release the clutch immediately after start then it is important that any resultant engine speed dip (or even stall) is not attributed to the start performance. The outputs from the start DSD can be divided into two categories, measurable signals which can occur multiple times in each data log, for example the flare speed of every start, or significant events which are identified and counted, for example crank and no start conditions. Each of these calculated values or counters can be associated with an upper and lower limit, these can be fixed values, a table versus another calculated channel or a threshold extracted from the EMS calibration. For calibration development where individual starts are analysed and processed, the results can be analysed in comparison to a limit. However, when analysing large quantities of data collected over a range of conditions, especially where the limit varies over these conditions, it is often more appropriate to normalise the data against the limit thus making it easier to identify trends and headroom against a changing target.

158

Reporting of IDAA analysis data

Figure 4 shows an example of the way that one of the key measures would be reviewed on a regular basis. Time interval from initial crank to reaching target speed is plotted against coolant temperature, vehicle odometer, time, barometric pressure and precranking battery voltage. Typically the acceptable limit for this time interval is dependent upon coolant temperature and changes significantly at low coolant temperatures, as shown in the plot of start time versus coolant temperature.

159

mRHLE Trend and Headroom Analysis Report Engine Start Crank To Target Speed Analysis vs Coolant Temperature

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160

SURGE ANALYSIS Measures of success for surge Surge is defined as an engine speed oscillation around the moving average engine speed. Figure 5 demonstrates this during a constant load acceleration. Engine Speed Response Plot

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Figure 5: Surge condition Definition of DSD - analysis scripting To effectively analyse vehicle surge, it is first necessary to identify the operating condition where it can be robustly measured. For example, to detect surge in a 'crowd' condition (driving with relatively constant intake manifold pressure) the DSD uses an algorithm to analyse driver's pedal input and engine load, waits for this condition to stabilise for a user defined time period and then analyses the engine speed trace to calculate a measure of surging. The DSD compares the instantaneous engine speed with a moving average engine speed and then performs a standard deviation calculation on the absolute value of this difference to define a surge measure. The operating conditions for the crowd are recorded in terms of engine speed and load and other relevant EMS parameters, e.g. ignition timing and coolant temperature. Further in depth analysis can be conducted using Fast Fourier Transforms to identify the frequency characteristics of the surge condition. As the DSD is able to identify any number of surge conditions throughout the datalog it can be used to analyse real world driving in addition to specific drive cycles. IDAA is particularly suitable to this real world situation as the incidences of crowd conditions over a wide speed / load range will be relatively low. Therefore it may take a significant time or distance to acquire sufficient data to cover the speed load range to be assessed.

161

Reporting of IDAA analysis data Figure 6 shows an analysis of two calibrations for surge in a crowd condition. The first is an existing production calibration with subjectively undetectable levels of surge at lower engine speeds moving to borderline acceptable at engine speeds greater than 4500rpm. The second calibration is based upon the first with some changes for demonstration purposes to induce surge in the 2000 to 3000rpm region at all loads. Subjectively this second calibration exhibited unacceptable levels of surge in the modified region. Figure 6 breaks down a specific area of concern into a range of measurement types including frequency analysis and also includes a section of the original data log associated with the concern. In the example differences between the two conditions can be seen in each of the graphs. The frequency distribution and peak power density analysis clearly shows the magnitude of excitations in the critical, for driver feel, range around 4Hz. There are clearly a number of objective measures that we can generate from the collected data which can be used to supplement the subjective judgements. In the longer term these measures may be correlated against the subjective judgement criteria so that the data can be presented in a more traditional format. In a real world situation the areas of surge may be more difficult to spot. Surge may only occur when specific operating conditions exist, for example during warm-up, with catalyst heating active, at certain speeds and loads or at a combination of conditions. The automated assessment approach outlined here means that all of the vehicle logs can be processed and any traces of surge under any conditions can be identified and analysed. To assist in the analysis of the surge data the reports can be easily modified to look at the data in a number of different ways. Alternatively the data can be filtered for specific conditions, for example the surge analysis could be reported against speed and load with and without catalyst heating active.

162

Standard deviation of engine speed difference from moving average shows a relatively constant trend for the original calibration and a peak around 2500rpm for the demo calibration

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IDLE STABILITY ANALYSIS Measures of success for idle stability We define base idle stability as the difference between engine speed and target idle speed in an idle condition without any external inputs. Figure 7 identifies some typical examples of idle instability.

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Figure 7: Examples of idle instability Definition of DSD - analysis scripting In contrast to long duration crowd conditions, idle conditions will tend to be encountered more frequently during real world driving and will be less influenced by driving style. Traditionally this would present the engineer with a huge quantity of idle data with reliance on the driver reporting the occurrence of idle stability concerns. For automated idle stability analysis the DSD initially searches through the entire datalog to identify each section of data where the engine is in an idle condition as defined by the EMS. In order to only analyse stable idle conditions without any transient effects associated with external or driver inputs, the DSD then applies a number of filters and timers. Similarly the transient condition of having just returned to idle can be excluded from the analysis. These stable idle periods are divided up into user definable fixed length intervals, for example 15 seconds. A further constraint is imposed upon the data in the 15 second window in that the engine load must remain within a threshold from the mean engine load; this allows the data to be rejected if uncompensated load changes occur on the engine which are not visible directly through the EMS. Once a valid 15 second window is identified the standard deviation of EMS calculated engine roughness and the corresponding mean load, engine speed, temperature, e.t.c. are recorded to produce a set of data for that 15 second period. Reporting of IDAA analysis data Figure 8 shows an example report format for idle stability using standard deviation of calculated engine roughness. In addition to reporting the roughness on a mileage and time basis, the analysis is also performed against oil temperature, coolant temperature, barometric pressure, engine load, ignition timing, catalyst heating status, air conditioning status and in either neutral or drive.

164

mRHLE Trend and Headroom Analysis Report Idle Stability Standard Deviation Of Roughness - 15 Second Window vs Odometer

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165

Drive

DISCUSSION This paper demonstrates the potential analysis capabilities available to the engineer using the IDAA toolset. Processing large quantities of complex datasets using conventional tools is very time consuming and therefore unrealistic, even for the skilled Matlab operator. Because of the difficulties in processing the data files the automated driveability analysis presented in this report would not normally be possible. This makes it difficult to compare IDAA to traditional approaches directly, clearly using IDAA it is now possible to make this kind of detailed analysis on an ongoing basis. To illustrate the efficiency gains of this technique we can compare the time taken to analyse 25 measurable characteristics from ten engine starts, each on separate datalogs, which in total takes under 20 minutes with the IDAA approach without user intervention. Manual assessment of the same data and outputs has been estimated to take over three hours of focused engineer's time. Another significant advantage with IDAA is the possibility to automatically analyse and generate reports from a large number of data files from various data sources. With the processing automated the engineer is able to invest time into defining new, more effective, ways of objectively looking at the system performance. In this example we were looking at the assessment of driveability data. In the introduction shortfalls to conventional driveability assessment were identified: • Reliance on sUbjective assessments • Limits accuracy • Comparisons difficult • Key opinion leaders influence ratings • Detailed objective assessments are very limited • Detailed track assessments carried out infrequently • Only in prevailing conditions • Many conditions not tested in detail • Limited use of objective data in assessment criteria • Subjective assessments can be broken down into objective measures • Potential assessment data collection opportunities not realised • Objective measures can be applied to any recorded data • If all vehicle drives were logged then there would be a vast resource of driveability data Using the IDAA toolset and high resolution data logging it is possible to take a significant step forward and to address these issues. Objective measures can be established to define the required output responses or performance requirements of the powertrain system. The benefits are clear for applying objective assessment criteria and automatically processing all of the data files available. The IDAA toolset has been successfully applied to a number of projects. The original application was for OBD fleet robustness, where it was used for validation. Several issues were identified, investigated and resolved that may otherwise have gone undetected. Some of these issues did not set fault codes, but were identified because the data showed they were approaching the operating or OBD thresholds.

166

CONCLUSIONS Validation of complex systems is a constant problem for vehicle manufacturers. The key to eliminating unexpected or undesirable behaviour is in the effective application of engineering experience during development and validation. The developer must be able to look at the 'right' things in the 'right' way. IDAA provides engineers the tools that allow them to look at as many functional performance measures as possible in the right way, and to do this consistently across projects. Using quantifiable Measures of Success established from vehicle development experience, IDAA has been used to effectively assess and report on key driveability areas often associated with customer complaints, including starting, idle control and transient response. © MAHLE Powertrain Ltd.

167

THE EFFECT OF EXHAUST AFTERTREATMENT AND ENGINE TEMPERATURE ON IOLS FOR CVT POWERTRAINS J A Gutierrez Magana and C J Brace Department of Mechanical Engineering, University of Bath

ABSTRACT An investigation has been conducted into the scope for Ideal Operating Lines (IOLs) to improve fuel consumption and CO, HC, NOx and PM emissions of a vehicle equipped with a CVT. This simulation was performed using the ADVISOR code within the MATLAB/Simulink environment. The ADVISOR code was modified to investigate the effect of a range of IOLs on a vehicle equipped with either a Spark Ignition (SI) or Diesel engine coupled to a CVT. The work addresses two issues often neglected during the calibration process due to the complex interactions involved. The work seeks to demonstrate that a simple simulation based tool is able to inform the calibration process at an early stage of vehicle development. First, the effect of the exhaust aftertreatment device on engine-out emissions for differing IOLs was analysed. In the case of the SI engine it was found that internal catalyst temperature increased more rapidly with the IOLs for HC and NOx. Consequently, maximum conversion efficiencies were reached more quickly than with the IOLs for fuel consumption and CO. As a result, IOLs derived with reference to engine-out emissions were unable to deliver the lowest tailpipe emissions results in some cases. Simulation of the entire system as demonstrated here is able to address this deficiency. The second area discussed is the analysis of fuel usage and emissions production as a function of engine temperature. An IOL-based optimisation approach is proposed to improve fuel consumption and NOx and PM emissions during the engine's warm up period. The approach was compared to a common technique of using IOLs calculated for hot operation and it was found that improvements of around 5% can be obtained for emissions. However, the scope for optimisation of fuel consumption was negligible due to the relatively stable fuel consumption characteristic during warm up.

INTRODUCTION The implementation of directives with the objective of limiting vehicles' emission levels has led to develop more sophisticated powertrains. As a part of this, development control strategies have been designed which allow the powertrain operation to emit less pollution and reduce fuel consumption, thus contributing to a reduction in the environmental impact caused by vehicles. The use of IOLs within the control strategy has long been shown to allow optimum fuel consumption to be achieved by CVT powertrains (1). It has further been shown that IOLs can be generated for other optimisation goals, such as exhaust emissions (2, 3, 4). Commonly, the effect of adherence to an IOL on driveability is neglected, which must be overcome in realistic control strategies. IOLs therefore are of most benefit in steady or near steady driving. A further aspect that is often overlooked is the effect of the exhaust aftertreatment device and the engine warm up characteristic.

169

This work is part of an investigation (5) into the scope of Ideal Operating Lines to improve the performance of CVT powertrains by addressing these shortcomings. In particular, this paper reports on a study into the effect of the exhaust afiertreatment device on emissions produced by the SI engine, and the effect of engine temperature on emissions and fuel consumption with regard to a Diesel engine. The simulation was performed in ADVISOR, which runs in the Matlab/Simulink environment. In addition, the work sets out to determine whether a relatively simple simulation approach can result in useful information to guide the calibration process. Such data would allow the calibration time to be reduced as iterative emissions testing could be minimised.

ADVISOR, SIMULATION PARAMETERS AND DESCRIPTION OF THE PLANT This section describes the main characteristics of ADVISOR and the models used in the simulations. Both spark ignition and Diesel engines were coupled to the same transmission and the same powertrain controller was used, with appropriate recalibration. Appropriate exhaust afiertreatment devices were selected for each engine type.

Advisor The 'ADvanced VehIcle SimulatOR' was developed by the National Renewable Energy Laboratory and it is a virtual tool designed to analyse performance and fuel economy of a wide range of vehicle types (6) including hybrid and conventional powertrains. ADVISOR version 3.2 was used for the work described in this paper. The simulator runs in the Matlab/Simulink environment and includes a graphical user interface which allows the vehicle's parameters and simulation characteristics to be defined. Vehicle systems and components models are included as code- and block-based Matlab and Simulink files respectively. It is possible to modify these models and adapt them to the objectives of a particular study. ADVISOR uses forward- and backwards-facing simulation approaches (6). The latter first calculates the requested road load and converts it into a set of requested torque and speed values through the wheels and the drivetrain components. This approach provides the basis to allow the powertrain operation to follow a particular IOL. The data set produced by the simulation includes a significant number of variables related to the vehicle's operation, allowing subsequent analysis. Simulation for this study was based on spark ignition and Diesel engine models coupled to a CVT, which are described later in this paper. A number of elements of the simulation are relevant to the work described here and are introduced below.

Vehicle and simulation parameters Simulation parameters include the characteristics of the vehicle, its accessories and cargo load, and the drive cycle employed to run the tests. The chosen vehicle model is based on a hypothetical small vehicle and approximates a 1994 Saturn SLl car, one of the standard models provided within the ADVISOR suite oftools. 170

The chosen wheel/axle model defines tyre, wheel, and axle assembly parameters for the specified vehicle type. Accessories load is defined by a model which defines a standard load drawn from the engine. Cargo load is set manually, and was defined as 80kg assuming that the vehicle would be driven by a medium-sized person. The EeE + EUDC drive cycle was chosen to perform all the tests in this study. ADVISOR models Spark ignition and Diesel engines

The selected SI engine model corresponds to a 1991 Oeo Metro 1.01 engine, for which fuel consumption and emissions data are available within ADVISOR. Maximum power and torque ratings are 41kW and 80.9Nm. Hot operating temperature is defined by the thermostat setting at 96° C and cold operation at 20° C. The model contains hot fuel consumption data and engine-out emissions maps in g/kWh and g/s units for HC, CO, and NOx. A 1999 VW 1.9L TDI engine model was selected for the Diesel engine Data for this engine is provided withint he ADVISOR suite. Maximum power and torque values are rated at 70kWand 217.2Nm. Hot and cold operating temperatures are defined as 99° C and 20° C. Hot and cold fuel usage and engine-out emissions maps in g/kWh and g/s for HC, CO, NOx, PM, and 02 are included. The model also contains cold and hot data for exhaust gas temperature in °C, total exhaust flow in gis, and total volume exhaust flow in m 3/s.

E z

J so

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Figure 1 Original and revised L TCs - VW Diesel engine The Limiting Torque Curve (LTC) of this engine exhibited a steep droop approaching maximum rated speed resulting in maximum power being achieved at somewhat less than maximum speed. As shown in figure 1, the LTC was revised slightly such that maximum power was achieved at maximum engine speed. This task was performed in order to better design and implement IOLs within the CVT powertrain control model, and was achieved by adapting the curve of a 92kW Diesel engine model through scaling and interpolation processes in order to keep maximum power and torque ratings at their original values.

171

Engine temperature prediction

As discussed in ADVISOR's fuel converter documentation (6), the engine's thermal model comprises cylinder, engine block, exterior accessories, and under bonnet temperatures. The model is a simple lumped parameter analytical representation of the engine in which the combustion generates heat, which is conducted through the engine block, and dissipated through liquid cooling, conduction, natural convection, and radiation. Engine coolant temperature is sensed and controlled by a thermostat, whose set point is user-definable. For the purpose of calculating the cylinder and engine block temperatures an energy balance is performed. The heat input to the engine is the remainder after the usable power and heat contained in the exhaust are subtracted from the energy contained in the fuel used. Heat is dissipated from the cylinder to the engine block by conduction. The conductance value is user-definable. A portion of the heat input is dissipated to the engine accessories through conduction. The remaining heat passed into the engine block is absorbed into the coolant fluid. Some of this heat can be used by the cabin heater and the remainder is dissipated by the radiator. A complete description of the engine temperature prediction is included in ADVISOR's documentation (6). Exhaust aftertreatment devices for SI and Diesel engines

Exhaust aftertreatment was accomplished with models which correspond to closecoupled conventional converters for hypothetical vehicles with SI and Compression Ignition (CI) engines. Both models include a scaling algorithm which scales the size of the catalyst as a function of maximum engine power. Zero-efficiency versions of these models were also created in order to obtain engine-out emissions. This emulates the use of unreactive converters experimentally for the same reason. 0.9 0 .•

0.3 0.2 0.1

400 600 Tempenrture ("C)

Figure 2 Warm up model of the catalytic converter for SI engines

172

0.9

0.8 0.7

0.6 0.5 0.4 0.3 0.2

-

CO efficiency HCefficiency ---" NOx efficiency

0.1

o·~~;;;;------;;0~~~~=PM~effi~Cje=",~y!. 600 800 Temperature (0C)

1000

1200

Figure 3 Warm up model of the catalytic converter for CI engines The catalyst for SI engines operates within a temperature range of -40 0 C to 12000 C. As shown in figure 2, which shows the catalyst's wann up model; maximum conversion efficiencies are 95% for CO, 85% for HC, and 91 % for NOx, and they are all achieved at 5500 C. In the case of the latter, an efficiency value of 90.09% is achieved at 415 0 c. The catalyst for CI engines operates within the same temperature range as for the previous converter although in use the exhaust temperatures observed are considerably lower. As can be seen from figure 3, maximum conversion efficiencies are 95% for CO, 91 % for HC, 45% for NOx, and 40% for PM. Maximum conversion efficiencies are achieved at 3000 C for CO and PM, 4500 C for HC, and 2000 C for NOx. Note that the conversion efficiency for NOx tails off at higher catalyst temperatures as the conditions needed for NOx conversion are not generally present in an oxidising exhaust stream (7). This simple model is not able to represent the chemical effects necessary to achieve passive NOx conversion in an oxidation catalyst, which depends on the quantity of stored HC on the catalyst as well as the temperature. The efficiency curve presented gives a fair approximation to the effect, however. Exhaust temperature prediction

As described in ADVISOR's exhaust system documentation (6), the exhaust system model is used to simulate an engine exhaust afiertreatment system, and consists of the exhaust manifold, downpipe, catalyst, and silencer. One of the outputs of the exhaust system model is the temperature of the components of the exhaust system and exhaust gas. The temperature of the catalyst is calculated through a lumped-capacitance approach. Mass and heat capacities are assigned to components such as the monolith, exterior shell, manifold and downpipe. Convective heat transfer coefficients from the hot exhaust gas to the components and consecutively to ambient air are calculated through heat transfer correlations. The heat released by exothennic reactions within the catalyst is estimated as a function of the mass of each emission component (HC, CO, NOx, and PM) being catalyzed and the conversion efficiency. This heat adds to the rate of converter wannup. Equations related to exhaust system modelling are shown below, a complete description can be found in ADVISOR's documentation (6): •

Change in temperature with time = [net heatflow] / [(mass)*(heat capacity)] 173

where net heat flow can be the sum of some or all of the following paths: • • • •

Convective = (heat transfer coefficient)*(surface area)*(surface-to-fluid temperature difference) Conductive = (thermal conductance)*(surface-to-surface temperature difference) Radiative = (emissivity)*(surface area)*(StefJan-Boltzman constant)*(bonnet temperature4 - cylinder temperature4) Exothermic = I(emission component mass flow) *(conversion efficiency)*(calorific value)

Continuously Variable Transmission

The CVT model chosen corresponds to a Subaru lusty Electronic VDT type Continuously Variable Transmission. Its rated speed and torque inputs are 5000rpm and 95Nm and peak efficiency is 95%. The transmission includes data at five pulley ratios, which are 2.5, 2, 1.5, I, and 0.5. The final drive ratio has a value of 5.83. Scaling factors for input speed and torque are included and were modified in order to match the transmission with the characteristics of each engine type. The factors were obtained by dividing maximum engine speed and torque values between maximum transmission's speed and torque inputs. Another modification required was the modification of the final drive ratio in order to better match the transmission to the Diesel engine. This adaptation was performed because the powertrain was unable to achieve the operating points required to follow an IOL. A 'trial-and-error' approach was used for this purpose and the best solution was obtained by dividing the final drive ratio by the torque scaling factor. Powertrain control

Powertrain control was carried out by means of a model designed to control the operation of an advanced conventional vehicle with a CVT. The model defines all powertrain control parameters, including gearbox, clutch, and engine controls. Transmission control starts by creating a vector defining a CVT power locus based on engine torque and speed ranges, and maximum power. This is followed by a series of computations which process a chosen engine map (normally fuel usage) and generate a vector defining the required CVT speed locus to minimise the engine map values at each point on the power vector. This CVT speed vector is termed a 'design curve' which is the basis ofthe control strategy for the powertrain and is actually an IOL. In practice this method generates very erratic IOLs as it is based on a simple minimisation of the desired engine map. This deficiency renders the use of automatically generated IOLs of limited use as their use in a vehicle would result in erratic driveability and would be impossible for the powertrain to follow due to the rate of engine speed change specified. As a result a key requirement for this work was the modification of the ADVISOR code to allow the generation and use of manually designed lines as described below.

174

IDEAL OPERATING LINE DESIGN IOLs for fuel consumption, CO, HC, and NOx were designed for the SI engine and IOLs for fuel consumption, NOx, and PM were designed for the Diesel engine. For this purpose, the respective maps included in the engine models were plotted as contour graphs in g/kWh units. ADVISOR's code was employed to define six contour levels, starting from the lowest value and ending up with a medium-range one. The maps are indexed by engine torque and engine speed, and the engine's LTC was included. Lines of constant power were also added, moving between zero and the maximum available power in twenty steps. Note that only ten lines of constant power are shown on figures for clarity. Following this approach, each constant power line represents increments of 4.1 kW and 7kWfor the SI and Diesel engine maps respectively. Drawing IOLs on engine maps A design tool was created in Matlab and was employed to graphically design the IOLs. This tool plots each engine map with the characteristics previously described and allows the designer to select the location of the points of a particular IOL. Once the points are located, they are joined together and their speed and torque coordinates saved as data vectors, which were later input to the control strategy. Each one of the points that constitute an IOL is located on a line of constant power. Because of this, more rounded shapes were achieved using twenty lines since smoothness was considered explicitly during the design process. All the IOLs were designed such that their last point is located at engine's maximum power point, which is located at the right end of the LTCs. As shown in figures 4 and 5, each IOL point was located by manually moving it along each line of constant power, labelled in figure 4 according to their values in watts and shown only as contours in figure 5. The best location was selected by placing each point inside the least-possible emissions or fuel consumption contour value as shown in figure 5 for the case ofNOx. Applied rigidly, however, this design approach would not produce a smooth trace because some emissions maps present very irregular contour shapes. In this case, compromises were made while locating the points in order to achieve a soft-rounded trace and some points were not placed on the best location. 90

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Figure 4 IOL point along a line of constant power

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Figure 5 IOL for NOx on NOx emissions map - Geo SI engine

.... -

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---
....

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Figure 6 IOLs for the Geo SI engine

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Figure 7 IOLs for hot operation of the VW Diesel engine The designed 10L to improve NOx emissions of the S1 engine is shown in figure 5. It can be seen that the 10L is constituted by twenty points and runs through the lowestpossible contour values. The complete set of 10Ls designed for this engine can be seen in figure 6, and the 10Ls designed for hot operation of the Diesel engine are shown in figure 7. Driveability Since driveability is affected by the control strategy applied to the powertrain, this aspect was also considered on the design of the 10Ls. Although this is a very subjective topic, the design objective was to avoid drastic torque and speed variation along the trace of the 10L so that engine speed can vary relatively softly and progressively.

176

Consequently, the compromises made to design soft and rounded traces can also have a positive effect on driveability. Also torque reserve, the extra power available by moving vertically from the 10L to the maximum torque curve, was considered. 10Ls exhibiting a higher torque reserve are advantageous for transient driveability, in marked contrast to typical fuel economy 10Ls which follow the maximum torque curve for much of the operating range. IOLs implementation in ADVISOR and resulting powertrain operation

Because the control strategy of the powertrain is defined by the CVT speed locus the code employed to generate this data was disabled as a first step to implement a user specified 101. However, the code used to create the CVT power locus vector was left unmodified. The following procedure uses as its basis the speed and torque coordinate data vectors generated for each 10L by the design tool described previously. From each pair of vectors, the one containing the torque coordinates was included in the control model. From this vector, a new CVT speed locus vector was defined by including a line in the model code which divided the CVT power locus vector by the torque-coordinates vector. Once the model-file is run, a 'design curve' which has exactly the same shape as the user defined 10L was created. This procedure was repeated for each variable and corresponding versions of the controller model were saved. 80

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-LTC _ Fuel con.IOL .0. Operating points • OutputatJafl. - - Constant power linn 500 1000 1500 2000 2500 3000 3600 4000 4500 5000 5500 6000 Speed (rpm)

Figure 8 Powertrain operation with IOL for fuel consumption - Geo SI engine

The effectiveness of each version of the control model was evaluated with a series of tests. From the simulation results, engine and output shaft operation data for each 10L was obtained. A typical result is shown in figure 8, which shows that the locations visited by the operating points were effectively situated on the 101. The negative torque operating points represent overrun transients.

THE EFFECT OF THE EXHAUST AFTERTREATMENT DEVICE

This section includes a study of the catalytic converter for the SI engine in order to analyse its effect on emissions with regard to the internal temperature predictions for differing 10Ls. Tests were performed with the simulation parameters and vehicle characteristics defined in section 3.

177

Internal converter temperatures during the drive cycle The variation of internal converter temperatures with differing IOLs during the drive cycle is shown in figures 9 and 10. It can be seen that temperature followed a similar trend with all the IOLs. In general, temperatures rise from the initial cold soak temperature to a range between 400 0 C to 5000 C in approximately 200 seconds. After this point temperatures increased in a more progressive manner during the ECE segments of the drive cycle and reached a maximum at almost 1200 seconds. This peak occurred because the engine was operated at full-throttle when it was required to accelerate the vehicle to reach maximum speed of the EUDC segment. The temperature rise is due to the very high CO production at that engine operating point, which substantially increased the heat released by CO when it oxidised in the catalyst. In practice this would be avoided, but for the work presented here the peak has little effect on choice of IOL.

Time (sec)

Figure 9 Internal catalyst temperatures with IOLs for fuel consumption and CO ~,---------------------~ 800 700 .00

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Figure 10 Internal catalyst temperatures with IOLs for HC and NOx As shown in figure 9, interior temperatures generated with IOLs for fuel consumption and CO were almost identical. The peak temperature for each IOL was about 8100 C. Figure 10 shows that that internal temperatures with IOLs for HC and NOx differ more significantly. The temperature generated with the IOL for HC was very similar to that with the IOL for fuel consumption, about 10 C higher during the drive cycle but with the peak approximately 25 0 C lower than that produced with the IOL for CO. In the case of the temperature produced with the IOL for NOx, temperature was approximately 30° C higher than that related to the IOL for HC, but the difference

178

increased to an average of 60° C after 900 seconds. This temperature profile is due to the higher engine operating speeds with this IOL. Conversion efficiencies during the drive cycle Emissions conversion efficiencies with differing IOLs on the drive cycle are shown in figures 11 to 13. Because conversion efficiency is directly related to converter temperature, the necessary time to achieve a maximum value also depends on the IOL employed. In all cases, particular conversion efficiencies increased more rapidly with the IOL for NOx, and rose very slightly quicker with the IOL for HC than with the IOLs for fuel consumption and CO. In the case of the latter, efficiency achieved an almost identical performance.

TIme (sec)

Figure 11 CO conversion efficiencies with each IOL 0.9

Time (SBC)

Figure 12 HC conversion efficiencies with each IOL

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Figure 13 NOx conversion efficiencies with each IOL

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The time taken for peak conversion efficiency for each emissions species using each IOL is summarised in table 1 below. It can be seen that the IOL for NOx produced a significantly different converter warm up profile due to the engine operating points selected. Table 1 Time to reach peak conversion efficiencies with differing IOLs

IOL

Time to reach peak conversion efficiency (sec)

Fuel con. CO HC NOx

888 890 888 386

Comparison of engine-out and tailpipe emissions levels for differing IOLs Engine-out and tailpipe emissions were compared in order to demonstrate that due to the previously studied characteristics of exhaust aftertreatment, the lowest specific tailpipe emissions were not delivered by the respective IOL in all cases. IOLs are commonly designed with regard to engine-out emissions data due to the difficulty in allowing for the effect of the catalyst at an early stage in the calibration process. Engine-out results were taken as a reference in order to evaluate the effectiveness of the improvements obtained with each IOL, and are shown in table 2. Table 3 shows the same results expressed as a percentage penalty when compared to the best result. With regard to engine-out emissions, it can be seen that the best results were returned with the respective IOLs as expected, leading to penalties of zero percent. The advantage of the IOL approach is clear, NOx can be improved by 15.6% if the IOL for NOx is followed in preference to that for fuel consumption. Using the NOx IOL, however will result in a 31 % penalty in engine out CO. Table 2 Engine-out and tailpipe emissions for differing IOLs Engine-out emissions

IOL Fuel con. CO HC NOx

Tailpipe emissions

(g/km)

(g/km)

CO

HC

NOx

CO

HC

NOx

7.024

0.891

1.412

1.188

0.380

0.200

6.956

0.884 0.860 0.875

1.403 1.392 1.222

1.189 1.226 1.336

0.377 0.370 0.350

0.195 0.195 0.170

7.235 9.109

180

Table 3 Percentage penalty in Engine-out and tailpipe emissions for differing IOLs

IOL Fuel con. CO HC NOx

Percentage penalty compared to best result Engine-out Tailpipe emissions emissions HC NOx CO CO HC NOx l.0

3.6

15.6

0.0

8.7

17.6

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2.7

3.1 31.0

0.0

14.8 14.0

7.9 5.7

14.9 14.6

l.7

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0.0

When tailpipe emissions are considered, however, the results are ranked differently due to the effect of the differing catalyst performance. The optimum CO result is obtained with the 10L for fuel consumption, but by an insignificant margin of 0.1 % over the 10L for CO. Pre- and post-catalyst CO emissions were very similar with 10Ls for both fuel consumption and CO. This is because the engine visited similar operating points during the drive cycles with these two 10Ls, due to their similarity. The position for HC is less straightforward. When observing pre-catalyst emissions there was almost no difference between the 10L for NOx and that for HC. When tailpipe results are considered, the faster light off when following the 10L for NOx results in improved HC performance, an improvement of almost 6% when compared to the 10L for He. In general the expected trade off between NOx and CO/Fuel economy is stilI strongly in evidence in the tailpipe data, showing the clear potential of the 10L approach to CVT control to allow significant control over powertrain emissions performance. This approach could be extended to include a dedicated catalyst warm up strategy where engine operation is optimised to maximise the energy availability to the catalyst. This could be simulated in the same manner if a map of energy availability were developed, over which an 10L could be plotted. THE EFFECT OF ENGINE TEMPERATURE This section comprises a study of the effect of engine temperature on fuel usage and emissions production. This effect is illustrated by consideration of the changing fuel and emissions performance at three specific power demand points which recur during the cycle. The effect of differing 10Ls on engine performance at these power demand points was investigated as a precursor to the consideration of cycle performance as a whole. Tests in this case were carried out for the Diesel engine model. Three operating points given by differing power demand values were selected after running a test with the 10L for fuel consumption calculated for cold operation. The procedure for selecting the points was based on an achieved torque output data vector generated. As can be seen in figure 14, three torque peaks corresponding to the three constant speed periods of each ECE stage were selected for investigation. Because each peak is repeated on each of the four ECE stages, the performance of the engine at these three operating points could be related to increasing engine temperature as the test progressed. 181

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Figure 14 Differing operating points on achieved torque output Fuel usage and emissions production for differing operating points as a function of temperature Fuel usage and emissions production for differing operating points were obtained by running tests with the IOLs for fuel consumption, NOx, and PM designed for hot operation. Since fuel usage was not included in the simulation results, it was necessary to modify the Simulink's fuel converter model in order to obtain this variable. As can be seen in figure 15, fuel usage increases linearly as engine temperature rises with the three operating points. The linear characteristic is due to the model structure used by ADVISOR. Hot and cold engine data are available and values at intermediate temperatures are derived by linear interpolation. This approach is a realistic compromise due to the volume of experimental data required. In a practical sense the slight increase in fuel consumption with temperature is due to negative effect on fuel consumption of progressively retarded fuel injection at elevated engine temperatures being more significant than the progressive reduction in friction in this instance. However, the increment was not very significant and this effect has less impact at a higher power level, which makes fuel usage relatively stable as a function of engine temperature. 0.B8 1!r----_~"8,·_--_

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Figure 16 The effect of temperature on NOx production with NOx IOL for hot operation

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Figure 17 The effect of temperature on PM production with PM IOL for hot operation With regard to NOx production, figure 16 shows that this variable decreases linearly with an increment of temperature at power levels of 9.93kW and 12.3kW, and increases linearly at 14.04kW, where NOx production is the lowest. This reversal in gradient at the higher power condition is due to the local gradient of the NOx map at this point. This is caused by the unpredictable interaction between engine friction, injection timing strategy and combustion characteristics. In the case of PM production, this variable decreases linearly as temperature rises with all three operating points, as shown in figure 17. However, this reduction is slightly less significant at higher power levels, where PM production decreases. Cold to hot engine maps and temperature compensated IOLs As stated previously, the ADVISOR model structure is such that fuel usage and emissions production are predicted to vary linearly as a function of temperature. Using this relationship engine maps at intermediate temperatures were calculated by establishing a proportional relationship between those given by cold and hot operating temperature. The temperatures at which the new maps were calculated were 40°C, 60 °C, and 80°C. The temperatures of the cold and hot maps were 20°C and 100°C.

183

250

200

150

--2000

2500

3000

3500

4000

4500

Speed (rpm)

Figure 1820 °C to 100° C IOLs for NOx - VW Diesel engine As these maps were calculated, 10Ls which derived from them were designed and implemented in the control model. These 10Ls were termed 'temperature-compensated 10Ls'. Figure 18 shows the set of temperature-compensated 10Ls for NOx. Similar families of 10Ls were produced for the other emissions components and fuel consumption.

Comparison of fuel consumption and emissions production with temperature compensated IOLs Fuel usage and emissions production results were compared in order to evaluate their performance with the temperature compensated 10Ls. At this stage the entire drive cycle was simulated with each 10L to allow the overall variations to be observed. Fuel consumption results are shown in table 4. As can be seen, this variable is very stable since it does not vary in a significant manner with the 10Ls. The best result is only 0.4% lower than the worst result.

Table 4 Fuel consumption with temperatnre compensated IOLs for fuel consumption

Fuel con. IOLs

Fuel consumption (1/100 km)

5.2587 5.2724 5.2543 5.2596 5.2515

Cold 40°C 60°C 80° C Hot

In the case of NO x production, the difference between the worst and best results is more significant, as show in table 5. With regard to engine-out emissions, the best result is 3.5% lower than the worst one whereas this number is 3.7% for tailpipe results.

184

Table 5 NOx emissions with temperature compensated IOLs for NOx NOx IOLs Cold 40°C 60°C 80°C Hot

NOx engineout emissions

NOx tailpipe emissions

(g/km}

(g/km}

0.6036 0.6252 0.6193 0.6147 0.6159

0.5044 0.5202 0.5163 0.5113 0.5238

Table 6 PM emissions with temperature compensated IOLs for PM

PM IOLs Cold 40°C 60°C 80°C Hot

PM engine-out emissions

PM tailpipe emissions

(gIkm)

(g/km}

0.0429 0.0414 0.0362 0.0366 0.0381

0.0347 0.0337 0.0292 0.0297 0.0309

With regard to PM production, it can be seen in table 6 that this variable experienced a substantial variation with each IOL. In this case, the best engine-out and tailpipe results are 15.61 % and 15.7% lower than the worst results respectively. Comparison of aggregate results with IOLs for hot operation An IOL-based optimisation approach was investigated. Data generated during the previously discussed tests was post processed to create new data vectors comprising the best fuel usage and emissions results with the temperature-compensated IOLs. This approach represents the operation of a controller which is able to dynamically select the IOL which returned the lowest fuel usage or emissions production as a function of engine temperature. The new data vectors were termed 'aggregate results' and were compared to those obtained with the IOLs calculated for hot operation.

The aggregate results vectors were produced by including the results obtained with each set of five temperature-compensated IOLs as columns in an Excel spreadsheet. Four such spreadsheets were developed to compare fuel usage, NOx, and PM production data. On each spreadsheet, a new column was created by selecting the lowest value within the five columns at every time instant. Therefore, this also represents an optimisation process for the engine's warm up period. The comparison shows how fuel usage and emissions production would perform with and without any optimisation during the warm up process. Results are shown as cumulative litres of fuel consumed and total emissions production during the drive cycle. As shown in table 7, totallitres of fuel consumed are in practical terms identical with both approaches. The best result is only 0.5% lower than the worst one. This characteristic is due to the stability of fuel consumption at different operating temperatures. 185

With regard to NOx production, table 8 shows that the optimisation approach provided a more significant improvement on this variable. The best result was returned with the aggregate result, which is 4.6% lower than that obtained with the IOL for hot operation. Table 7 Total Iitres offuel consumed Approach 1000 C Fuel con. IOL Aggregate result

Litres 0.5797 0.5767

Table 8 Total NOx production Aeeroach 100 °C NOx IOL Aggregate result

Grams

6.7307 6.4225

Table 9 Total PM production Approach 1000 C PM IOL Aggregate result

Grams 0.4167 0.3885

PM emissions production showed a larger improvement with the optimisation approach, as shown in table 9. In this case, the result obtained with this approach is 6.8% lower than with the hot IOL.

CONCLUSIONS

Two issues are addressed in this paper: the effect of exhaust afiertreatment and engine temperature on the improvements returned by IOLs designed to optimise fuel consumption and emissions production on CVT powertrains. It is clear that the warm up characteristics of the exhaust afiertreatment device affect the

improvements delivered by IOLs. As a result of this, the lowest CO and HC tailpipe emissions results were not obtained with their respective IOLs. The differences were significant in the case of HC, with a 5.7% improvement in HC achieved by following and IOL for NOx. It may be that even greater improvements are possible if a dedicated catalyst warm up schedule is followed during the early part of the cycle but to be of greatest benefit any such optimisation should include manipulation of engine control variables to maximise the enthalpy in the exhaust flow. Such a strategy could be demonstrated using the techniques described here. Engine temperature also plays an important role in the optimisation of fuel usage and emissions production. The optimisation approach proposed demonstrates that NOx and PM emissions can reduced by approximately 5% if the IOL is dynamically optimised during the engine's warm up period. However, the improvement on fuel consumption was negligible as the optimum engine speed for fuel consumption remains relatively constant for the engine under consideration regardless of the operation temperature. As a necessary step to investigate these effects a design tool was developed to allow user defined IOLs to be produced and a procedure for implementing these IOLs in

186

ADVISOR was developed. These modifications allow the use of ADVISOR as a calibration aid early in the development process with the aim of reducing iterative emissions testing in hardware.

REFERENCES PWR Stubbs, JM Ironside, 'Microcomputer Control of an Automotive Perbury Transmission', Froc. ImechE, 19S1, C200/S1 2 CJ Brace, M Deacon, ND Vaughan, RW Horrocks, CR Burrows, 'An Operating Point Optimiser for the Design and Calibration of an Integrated Diesel/CVT Powertrain', Proceedings of The Institution of Mechanical Engineers Journal of Automobile Engineering (Part D) Vol. 213, May 1999, pg 215-226, ISSN: 0954-4070. 3 CJ Brace, ND Vaughan, CR Burrows, 'The compromise in reducing exhaust emissions and fuel consumption from a Diesel CVT powertrain over typical usage cycles', CVT'99 - International Congress on Continuously Variable Power Transmission, September 16-17, 1999, Eindhoven University of Technology, World Trade Centre, Eindhoven, The Netherlands 4 CJ Brace, 'Integrated Powertrain Design and Control', Business Briefing Global Automotive Manufacturing & Technology 2000, World Markets Research Centre 5 JA Gutierrez Magana, 'The Ideal Operating Line Concept and the Effect on Emissions and Fuel Consumption'. Thesis (MSc). 2005, University of Bath. 6 A Brooker, T Hendricks, V Johnson, K Kelly, T Markel, M O'Keefe, S Sprik, K Wipke, 'ADVISOR 3.2 Documentation', 2001. 7 J TRussell, B Wedekind, 'An investigation into strategies to increase the catalytic reaction of a passive DeNOx catalyst on a Diesel Passenger Car', Paper No. 97A21122, presented at 6th European Congress~Lightweight and Small Cars the Answer to Future Needs, Cernobbio, Italy, 1997

© University of Bath

187

A TURBOCHARGED DIESEL ENGINE WITH INTAKE AIR ACCUMULA TOR TO IMPROVE TORQUE TRANSIENTS IN A CVT POWERTRAIN Y. Rohrbacher, B. Bonnet, and N. D. Vaughan Cranfield University

ABSTRACT One of the main advantages of Continuous variable transmissions (CVT) is to reduce fuel consumption and emissions by running the engine at its maximum efficiency. This often involves overdrive ratios to keep engine speed low under cruising conditions thus compromising vehicle longitudinal response during tip-ins; in fact an additional torque is required to accelerate the engine to an operating point where higher power is available. This raises some driveability compromises especially for turbocharged diesel engines on which the transient torque response is slow due to turbo lag. This paper presents an AMESim [I] model of a 3.0L V6 turbocharged diesel engine paired with a CVT [2]. An air accumulator is implemented to improve the transient torque response by injecting air into the intake manifold during tip-in manoeuvres at low engine speed. A number of accumulator initial conditions and controller calibrations are investigated for a given transient manoeuvre. The system has shown a significant improvement in the transient response for part and full load tip-ins. The subsequently enhanced torque controllability permits design trade-offs for reduction of the turbocharger lag. The benefits of adequate control strategies were demonstrated, resulting in faster vehicle acceleration response and potentially better driveability. INTRODUCTION It is evident that the requirements for engine operation when matched with a CVT

powertrain are different to those of a conventional discrete ratio manual. The steady state operating conditions will be significantly more biased toward lower engine speed and higher loads to take advantage of potential fuel savings. The consequence for a tipin demand is that the engine may limit transient response hence not satisfy the driver's performance expectations. In this region of engine operation any transient is limited by the available air charge and it is the intention of this investigation to use an additional air input to assist in transient torque production. The simulation of complete powertrain behaviour under transient condition involves a large amount of modelling over a range of physical domains. All parts of the system have to be modelled so that they describe the plant to an adequate level of detail. Further more, the control strategy needs to be designed realistically and to an equivalent level. The modelling objectives were satisfied by the usc of the model library in the simulation package AMESim [I] and the control strategy which were implcmcntcd in Simulink.

189

The plant and the controller are described in the next two sections respectively. The assumptions and simplifications involved are explained and the initial simulation results show the effects that an aggressive transient has on a conventionally controlled plant TURBOCHARGED DIESEL ENGINE MODEL

The simulations have been carried out within the AMESim environment. AMESim is a multidisciplinary piece of software performing ID transient simulation using object oriented modelling and is appropriate to simulate the transient response of the system components including the engine. The variable step solver allows for different physical environments to be integrated into one single simulation. AMESim features a specific library developed by the Institut Francais du Petrole to model internal combustion engines. It was used together with the mechanical and driveline libraries to build the complete powertrain model. The engine fluid flow is described by three gaseous and one liquid phase: • Air, which is considered as an ideal gas • Fuel in the vapour form • Burned fuel, i.e. the main components of exhaust gases • Liquid fuel The properties of these fluids have been taken from the AMESim® data base for diesel fuel. The fluid dynamics are computed using pipe and volume models which take into account compressibility and inertia. The following sections will detail the engine and driveline models. V6 TDI engine The engine is a 3.0L V6 TDI based on the Audi Wl9 engine [1]. The principal dimensions of the engine architecture, timing and combustion model parameters are shown in Table I. The engine has a 4 valve head supplied from a single variable geometry turbocharger. The fuelling system has been simplified by removing the detail of the common rail and high pressure pump, and is described by simple injectors operating at a constant pressure of 1600bar (Figure 1). Also the multiple injections per stroke achieved on the Wl9 engine by the piezo-technology have been replaced by a single injection. ~
9

7

Injector control vmiables

;-.~ Injector

~ ~~ .=G ---~.

'"

Cylinder head

...-- Combustion chamber Thennal exchange Crank assembly

Figure 1 Single cylinder representation of the engine model

Additional simplifications were made for the intake and exhaust systems in order to reduce the computing time whilst keeping the accuracy of the results to an acceptable

190

level. The following sections explain the modelling strategy when real data were not available. Whenever possible the model parameters were taken from the AMESim TDI demonstration model. Combustion model

The combustion heat release is calculated using the extended Chmela based model [2] rather than the Twin Wiebe's model, which is also available in AMESim. This was chosen as it is a physical model that is more predictive and provides better results especially for transient simulations. In this model, the heat release is calculated with respect to the mass of fuel present in the chamber and the turbulent kinetic energy k, which is considered to be only induced by the fuel spray. Equation 1 shows how the rate of heat release is parameterized by the factors Crate and Cdiss . The latter is not shown here but is present in the calculation of k. The relation between the turbulence and the heat release are defined by Cmode . These parameters are mainly dependent on the combustion chamber geometry and fuel air mixing quality. The parameters were taken from the AMESim data base and are shown in Table 1. (1)

where k is the turbulent kinetic energy, mf is the mass of fuel available in the combustion chamber and Q,omb is the heat released.

A is a factor that takes into account the effect of the residual exhaust gases on the combustion heat release with XSGR the molar fraction of burned gases in the mixture before the combustion and UR a variable adjusting the effect of the residual gases on the heat release. This factor A is of main importance when using EGR and a turbocharger because of the exhaust back flow since both play a significant role on the engine transient torque. Table 1 Engine architecture, timing and combustion model parameters Engine Block model data Units Values Parameters Engine Size L 2.97 Number of Cylinders 6 Layout V mm 83 Bore Stroke mm 91.4 Con rod length mm 150 Compression Ratio 17 Firing order 1-5-3-6-2-4 Equivalent Inertia kg.m' 0.25 kJ/kg/Degree 450 Cmode s 0.00696 Crate Cdiss Hz 150

Intake & turbocharger

The intake system is modelled by pipes, volumes and special components simulating the intercooler and associated heat exchange. Only straight pipe models have been used. The ducting upstream the compressor is modelled by a pipe of 1m length, which creates

191

a negative relative pressure at the compressor input. The air filter has not been modelled. The air charge provided by the compressor is cooled by an air to air intercooler. The intercooler is modelled by a special volume that takes into account the pressure drop induced by the system depending on its geometry, and the heat loss. This heat flux is calculated in respect to the mass flow of fresh air going through the heater which depends on the vehicle speed and the heater geometry. This geometry was tuned to get a maximum output air temperature around 50°C at the engine maximum power and a pressure drop corresponding to those achieved by a typical air-air intercooler. The cooled air flows in two intake plenums, one for each bank, which are directly fixed on the cylinder head. The volume of each plenum is 1.5L that gives a plenum volume to bank displacement ratio of 1. The turbocharger model is composed of three parts: the compressor, the turbine and a rotary inertia (see Figure 2). A close approximation of the real turbocharger inertia value (30.1O-6kg.m2) has been taken. This inertia is an important parameter since it limits the turbocharger response during the transient operations. Both compressor and turbine behaviour are modelled by maps defining the turbocharger operating conditions over its speed range. These maps are established by the manufacturer and are actually expressed in terms of corrected mass flow and corrected speed. As it is a variable geometry turbocharger, the turbine map is composed of a combination of the turbine operating points for different positions of the turbine blades. In the present case these maps were not available and were built from the AMESim TDI demonstration model. The compressor map was then tuned to meet the engine performance requirements. Gas dynamics are also taken into account in the model thanks to geometrical considerations which allows simulation of the turbocharger surge phenomenon. The exhaust gas back flow is also simulated, which is of importance during transient simulations since an increase of the exhaust pressure increases the back flow, hence reducing the engine output torque [3].

Figure 2 Turbocharger model

192

Exhaust & EGR The exhaust system has been modelled in the same way as the intake system, using elementary pipe and volume components. Two 1.5L exhaust manifolds are directly fixed on the cylinder head feeding the turbocharger turbine via two 500mm long pipes. The exhaust line downstream of the turbine has been simplified and is modelled by a pipe filling a 3L volume which reproduces a silencer; a final pipe of 2m is connects this volume to atmospheric air. The exhaust line establishes the turbine downstream pressure. The exhaust gas heat loss is simulated in the exhaust system to take into account the loss of energy recoverable by the turbine as well as the exhaust manifold thermal response, hence the turbocharger lag [4, 5]. The EGR is modelled by a variable area vane, placed just before the turbine input, that controls the fraction of exhaust gases being re-circulated. These gases are cooled by a water to air intercooler before being injected directly in the plenums. The cooling of the re-circulating burned gases limits the increase of the inlet charge temperature and hence is limiting the torque loss. The engine transient torque is affected by the EGR system since even if the vane is closed instantaneously; exhaust gases are still remaining in the intake manifold and an increase in the exhaust manifold pressure increase the exhaust backflow in the cylinders [3].

Engine model tuning and validation The parameters for each component of the model were taken from real life data when available or from the IFP Engine database. It was not intended to accurately copy the Audi W19 engine in simulation, but a coherent engine model was required. For this reason, real-life test bed data for both the turbocharger and the engine were used to tune the model to reproduce the W 19 full load performance. The extensive full load data included for instance a number of key temperatures, fuelling control, mass flows, turbocharger speed, engine output torque. The main parameters modified during the calibration of the model were dimensions of intake and exhaust, combustion parameters, valve profiles, heat exchange and friction coefficients [I]. CVT & Driveline model During tip-in manoeuvres, the engine is accelerated as quickly as possible to its new demanded speed, which widely affects the engine transient response. Moreover, this acceleration is related to the difference between the engine torque required to keep the vehicle at its constant speed and its actual output torque [6]. Therefore, a driveline and a vehicle model have been implemented. As the hydraulic time response of the CVT is smaller than the engine response and very accurate engine acceleration simulation is not required, the CVT is modelled by a simple variable ratio component. The CVT ratio is directly fixed by one of the component inputs. Consequently, the CVT controller has to define the evolution of the CVT ratio. Final drive and CVT efficiency are modelled by an additional fixed ratio gear train at 85 % efficiency. This transmission model connects to a vehicle model that determines the vehicle longitudinal acceleration and speed from the wheel torque equilibrium. The model computes the vehicle resistive torque by adding the rolling resistance, the aerodynamic drag and the slope resistance. The vehicle initial conditions and the initial CVT ratio are finally set to define the initial engine operating point and vehicle speed.

193

CONTROLLER The controller for the plant described in the previous section was entirely built in Simulink. The AMESim and Simulink solvers were used in a co-simulation exchanging their respective input and outputs through the interface. This means that the state variables are not exchanged, which is appropriate for this plant / controller split. The engine controller is made of a supervisory block which sets targets for the fuelling, turbocharger, EGR and air injection control blocks. Calibrated tables fix the required injection mass and duration, air charge and intake pressure. These look up tables are based on engine load and speed operation conditions. Fuelling strategy The fuelling strategy sets the injector opening times in the EMS supervisory block to determine the fuel mass flow required per stroke. A fuelling map was therefore calibrated to match the real-life engine's full load characteristics of torque and air fuel ratio. The duration of the single injection is scheduled in a similar way in order to provide the required exhaust pressure whilst keeping the combustion rate of heat release and peak pressure to a minimum to respect NOx and noise emissions. Turbocharger and EGR controller architecture The control of the variable geometry turbocharger and EGR valve during speed and torque transients is the key to good performance and satisfactory emission levels. Their response is very much affected by the exhaust pressure hence making both systems particularly difficult to control. The approach taken is to control the turbine around a calibrated set point (feed-forward) [3, 7]. The intake boost pressure at the compressor stage is then close-loop controlled by varying the position with a PI controller. The gains are calibrated over the speed and load range from plant response measurements.

l' gain mllp

1gain map

+---~

-+-----+

Pl Controller

-+---~-~---4

VGT actuator - l - - - - - - - + set points ~_ _ _ ..

Figure 3 Turbocharger actuator controller

194

Feed-forward set point

Figure 3 shows the turbocharger control architecture with the feed forward set points map at the centre, PI controller and associated gain maps (top). Provision is also made to override the actuator and boost pressure set points blocks during a simulation to investigate the plant response at a particular operation condition. CVT control The purpose of this study was not to investigate the influence of the CVT control strategy on the engine response, but to match the latter to a powertrain requirement during a given manoeuvre. Consequently the CVT controller was kept relatively simple in order to provide a way to reproduce typical driving manoeuvres such as tip-ins. The CVT control law is based on the ratio derivative control. Jantos [8] has demonstrated the need to limit the rate of change of the transmission ratio during a speed transient. Large tip-ins are associated with a new engine speed request to match the power requirement. This means that extra engine torque is required: • To accelerate the vehicle • To accelerate the engine and transmission input shaft. If the CVT ratio was decreasing too quickly during the acceleration phase, the vehicle would decelerate as a result of the lack of engine torque. The controller is therefore composed of a steady state block which maintains a desired ratio during cruising for constant engine speed and a transient block (Figure 4) which uses engine brake torque as a feed back to optimise the engine acceleration phase [8]. Consequently during a tip-in manoeuvre, the engine will accelerate to a scheduled speed set point using no more than the available engine torque that builds up as the request has increased. The advantage of this approach is that any torque response benefits will be directly reflected in the engine speed transient which systematically makes the most of the available torque margin. This means no particular calibration and a powertrain biased towards best performance. However unrealistic the control law may seem, it highlights the benefits an improved engine response as over the calibration scope for a more realistic powertrain control strategy. Figure 5 shows a tip-in manoeuvre and its subsequent engine speed change and torque profile.

Figure 4 CVT transient control law Initial simulation results

There are a number of driving manoeuvres which could be used to benchmark the performance of the plant. The most demanding from a control point of view is one that involves both a large speed and torque transient. Therefore most of the simulation results presented will be those of a tip-in from 30% engine load at 50 kmlh and 1200 rev/min to full load, 4000 rev/min. Although this represents an extreme manoeuvre it is

195

the condition where a driver might experience the greatest dissatisfaction with the system response. This is not necessarily the most representative manoeuvre but it is the most likely to exhibit potential issues. The manoeuvre is shown in Figure 5 and Figure 6. It can be seen that Lambda goes from one steady state value to another and drops under the stoichiometric value for about a second. This is detrimental for emissions and particularly smoke levels. This situation is created by the rapid change of requested torque combined with an unusually large speed transient. In this situation the turbocharger control cannot respond quickly enough to the new boost pressure request. This phenomenon called turbocharger lag is due to the air path configuration around the combustion chamber. As the fuel mass injected per stroke increases, the exhaust gas energy increases and provokes a temperature transient in the exhaust system as well as increasing the work transmitted to the turbine. This part of the system response penalises the acceleration time of the turbocharger. A more conservative fuelling strategy could limit the drop in air fuel ratio during the manoeuvre but it would also compromise the torque response hence directly altering the engine acceleration phase duration. This would result in an extended vehicle acceleration delay after the tip-in.

Figure 5 Tip-in transient

Figure 6 Lambda excursion

AIR INJECTION Design & Modelling

To compensate for the turbocharger lag and boost the engine transient torque, it is proposed to inject air into the intake manifold during tip-in manoeuvres. An air accumulator is used to store pressurized air which is released through a vane controlling the accumulator output mass air flow. The main issue of the system is the quantity of air that can be stored which is a function of the accumulator size and the charge pressure. As it is better to install the accumulator close to the intake manifold to avoid pressure loss and for a faster system response, this latter has to be placed under the bonnet. Consequently, the accumulator size has been fixed to 5L which is assumed to be the maximum volume that can be fitted in the engine compartment. The accumulator initial pressure depends on the feeding system. Two possibilities main are considered: • Fill the accumulator directly by the turbocharger during back off events. This potentially allows a reduction of MAF overshoot and provides the energy to recharge the accumulator at little cost.

196

Use a disengageable pump driven by the crankshaft in a similar way to a supercharger. During engine braking events some of the extra torque can be used to pump air into a second orifice of the accumulator in a regenerative process. The feeding system has not been modelled but 2 bar to 6 bar initial pressure have been taken which can be obtained by the turbocharger and the pump respectively. The Manifold Air Injection (MAl) system is modelled by a volume of 5L connected to the intake manifold downstream of the turbocharger compressor, via a variable area valve as shown by Figure 7. The accumulator feeding system is not modelled and the volume component is set to initial conditions of pressure, temperature and by the ideal gas law a fixed density [2]. The accumulator output mass air flow is calculated from the pressure ratio between the intake volume and the accumulator, the temperature inside and the area of the valve.

BGR Virtual Sensor

M.A.l valve MAl volume

Heat exchange

Pressure sensor Pressure sensor

Figure 7 Air injection model Control strategy The air path of the engine is a complex system characterised by non-linearities in the turbocharger response and the EGR system. As a consequence, it is not straight forward to define the best opening and timing of the MAl valve. In an effort to try and decouple the effects of turbocharger and EGR, the later was initially disconnected during the simulation runs. It is not expected that the ROHR and NOx level will be degraded since the air injection only makes up for a lack of air in the mixture. The initial controller was not calibrated to limit the rate of fuel increase and control the smoke levels in order to highlight the effects of the lags in the air path. During the transient, the compromise between the amount of air provided respectively by the MAl system and the turbocharger largely involves optimising the airflow through the compressor: • Too much MAl would increase the pressure in the intake plenums hence the pressure ratio across the compressor. This results in a degradation of the compressor efficiency and eventually going into the surge region. Too little MAl would mean that the volume empties without a significant contribution to the air charge into the engine. This results in a poor performance of the system. The controller architecture adopted is presented in Figure 8. The MAl valve is closed during steady state operation and transients are detected based on the IMAP error

197

controlled by the turbocharger PI. If the error goes above a calibrated threshold, the MAF error is the input to the MAl valve controller. The benefit of using the IMAP error instead of the MAF error is that the boost pressure error builds up almost instantaneously after the tip-in, where the MAF error is much more related to the engine speed which increases during the engine acceleration phase.

.,-

First order filter of MAF e1'l'or

First order /.iller of output

.'<11IIIIIIII

-....

Trigger based threshold

()Jl

[MAP error

Controller gain

Figure 8 Manifold Air Injection controUer The MAF error is then first order filtered in order to smooth the MAl control output as large steps proved detrimental to the turbocharger performance. The valve opening is proportional to the MAF error. This is therefore a simple open loop proportional control for which the gain would have to be calibrated over the operating region. This is an initial strategy which allows investigating all aspects of the control, as highlighted in the following section.

Figure 9 Surge limit on turbocharger operation with MAl

198

Simulation results & discussion Surge limit

The limit to how much of the MAF error can be compensated for with the MAl is largely due to the turbocharger surge effect which is suggested in Figure 9. The dotted line on the left hand side of the plot is the surge line beyond which the MAF becomes too low for the compressor to function properly. It is highly undesirable to let the turbocharger reach this region. The direct effect of this phenomenon is not possible to inject a large amount of air early in the transient. A small amount is acceptable, as seen in Figure 9 with a number of controller gains investigated. The mass air flow out of the MAl has then to be held as the turbocharger accelerates. Storage pressure

The amount of air available in the MAl is also crucial. Not only does it affect the capacity of the system to sustain a long lasting transient, but it also affects the air flow response of the MAl as the pressure ratio across the control valve drops as the volume empties. Figure 10 shows how the mass of air present influences the potential improvement in torque and lambda during the transient. The differences are further highlighted by the relatively simple control strategy which does not account for the MAl initial conditions, or attempts to regulate to valve opening to maintain a target mass air flow across it as the pressure drops inside the MAL The IMAP error is an input to the turbocharger PI controller and the ideal lambda is calculated from the ideal MAF and the actual fuel flow.

2400

18

~ 2200

1.6

~, 1.4~==C\. . g

~ 2000 ~ 1800

...J

1.2

1BDO 0.8

500,----,----,-----,-----, 400

:t

300

~ 200

>-

100

Time Is]

Time Is]

Figure 10 IMAP, Lambda, TrqEng & SpdEng at different MAl fllling pressures

199

Gain Sensitivity

The results presented in Figure 11 show that there is an optimum trade off to be found for the choice of the gain for the valve control. On the one hand, it can be seen that the gain largely affects the rate at which AMAP (bottom right) drops during the boost sequence. On the other hand, the benefits in lambda (top right) are not as significant. A gain of 3 % per gls of MAF error was found acceptable for most of the tip-ins simulated. Driving manoeuvres

The Manifold Air Injection is here compared with the baseline simulations, i.e. the same tip-ins without the use of MAL The benefits of the MAl system can be observed for all tip-ins in Figure 12. The tip-ins shown are from 1200 rev/min, 50 krnlh and 30% engine load. The request is then for 100% load at 2000, 2500, 3000, 3500 and 4000 rev/min. The right hand plot shows the engine speed profile where the increased engine torque during the transient helps reduce the acceleration time. For the most aggressive tip-in, the engine acceleration time is reduced by 12 % whilst keeping lambda well above the baseline value. 1.5

2&)0

1.4

2400

1.3 2200

1.2

j

,1

~

~

2000

1.1

1800

0.9 1600

0.8 0.7 2

1400'2

2 %/(gls} ·-··"2.5%/(g/s}

--3%I(g/') -3.5%1(91$) -4O/gJ{g/s)

lime lsI

Time Is]

Figure 11 IMAP, Lambda, MAF & AMAP at different proportional gains

200

2.4

22

1.8

Baseline ~OOO ray/min Baseline 2500 rev/min Baseline 3000 rev/min --_ ... ' BaseliRe 3500 rev/min ----. Baseline 4000 rev/min MAl 2000 rev/mi n MAl 2500 revlmm MAl 3000 reV/min - - MAl 3500revlmln - - MAl4000'revJrhin

10D 150 MAF[glsJ

200

250

Figure 12 Comparison between baseline and MAl setups CONCLUSIONS A complete powertrain model has been setup and exhibited the expected phenomena when subjected to large speed transients under a tip-in manoeuvre. The issue is related to a starvation of air due to the incapacity of the turbocharger to match the required boost pressure early in the manoeuvre. This resulted in an over rich mixture because a more conservative fuelling strategy would compromise the torque rise that is necessary to reach the required engine speed. After implementing a manifold air injection system in the plant, a controller was built to help improve to exhaust mixture properties and the torque profile during a demanding tip-in. Based on the turbocharger MAF error and triggered by the IMAP error, air was injected directly into the intake manifolds from a pressurised volume. The benefits of the device are summed up in Figure 13 with the significantly improved lambda profile. The area under the critical value of I is almost halved while the vehicle speed profile shows a small increase in performance. Further work would involve a more advanced coordination of turbocharger, MAl and EGR controllers and an extended range of transient manoeuvre simulations. It will also be necessary to investigate the charging system to minimise the energy requirements for pressurisation of the accumulator.

lo:r . i o

0.5

1.5

!

!C! 2.5 Time'[s}

Tim~

3

3.5

t-i

=-11 4.5

5

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Figure 13 Comparison between Baseline & MAl simulations

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REFERENCES

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IPDS 2006 Integrated Powertrain and Driveline Systems 2006

CIMA Study Systems 2006: Integrated Management (CIMA Study Systems Managerial Level 2006)

2006

2006)

2006

Architecting Dependable Systems 4 conf., ICSE 2006 and DSN 2006

CIMA study Systems 2006 : Organisational Management and information Systems (CIMA Study Systems Managerial Level 2006)

Topology and groupoids (2006)

2006 (239)

2006 (220)

2006 (219)

2006 (240)

2006 (236)

2006 (228)

Newsmakers 2006

2006 (221)

ПДД 2006

2006 (224)

2006 (234)

2006 (223)

Bash.org.ru 2006

August 2006)

2006 (225)

2006 (230)

AutoCAD 2006

2006 (235)

Copperhead.2006

Dermatopathology (2006)

2006 (238)

2006 (229)

Comparative Genomics, RECOMB 2006, RCG 2006

IPDS 2006 Integrated Powertrain and Driveline Systems 2006

CIMA Study Systems 2006: Integrated Management (CIMA Study Systems Managerial Level 2006)

2006

2006)

2006

Architecting Dependable Systems 4 conf., ICSE 2006 and DSN 2006

CIMA study Systems 2006 : Organisational Management and information Systems (CIMA Study Systems Managerial Level 2006)

Topology and groupoids (2006)

2006 (239)

2006 (220)

2006 (219)

2006 (240)

2006 (236)

2006 (228)

Newsmakers 2006

2006 (221)

ПДД 2006

2006 (224)

2006 (234)

2006 (223)

Bash.org.ru 2006

August 2006)

2006 (225)

2006 (230)

AutoCAD 2006

2006 (235)

Copperhead.2006

Dermatopathology (2006)

2006 (238)

2006 (229)

Comparative Genomics, RECOMB 2006, RCG 2006

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