Modelling, Simulation, Testing, and Optimization of Advanced Hybrid vehicle powertrain-混合动力汽车

Modelling, Simulation, Testing, and Optimization of Advanced Hybrid

Vehicle Powertrains

By

Jeffrey Daniel Wishart

B. App. Sc., University of British Columbia, 1998

M. Sc., University of Saskatchewan, 2001

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

in the

Department of Mechanical Engineering

?Jeffrey Wishart, 2008

University of Victoria

All rights reserved. This thesis may not be reproduced in whole or in part, by photocopy or other means, without the permission of the author.

ii

Modelling, Simulation, Testing and Optimization of Advanced Hybrid

Vehicle Powertrains

By

Jeffrey Daniel Wishart

B. App. Sc., University of British Columbia, 1998

M. Sc., University of Saskatchewan, 2001

Supervisory Committee:

Dr. Zuomin Dong, Supervisor, Department of Mechanical Engineering

Dr. Andrew Rowe, Departmental Member, Department of Mechanical Engineering

Dr. Peter Wild, Departmental Member, Department of Mechanical Engineering

Dr. Subhasis Nandi, Outside Member, Department of Electrical Engineering

Dr. Jie Chen, External Examiner, Indiana University Purdue University at Indianapolis

iii Supervisory Committee:

Dr. Zuomin Dong, Supervisor, Department of Mechanical Engineering

Dr. Andrew Rowe, Departmental Member, Department of Mechanical Engineering

Dr. Peter Wild, Departmental Member, Department of Mechanical Engineering

Dr. Subhasis Nandi, Outside Member, Department of Electrical Engineering

Dr. Jie Chen, External Examiner, Indiana University Purdue University at Indianapolis

Abstract

The internal combustion engine (ICE) vehicle has dominated the transportation market for nearly 100 years. Numerous concerns with continued use of fossil fuels arise, however, and these concerns have created an impetus to develop more efficient vehicles that release fewer emissions. There are several powertrain technologies that could supplant conventional ICEs as the dominant technology, most notably electric and hybrid powertrains. In order to achieve the levels of performance and cost of conventional powertrains, electric and hybrid powertrain designers must use design techniques and tools such as computer modelling, simulation and optimization. These tools facilitate development of a virtual prototype that allows the designer to rapidly see the effects of design modifications and precludes the need to manufacture multiple expensive physical prototypes.

A comprehensive survey of the state of the art of commercialized hybrid vehicle powertrains is conducted, and the term multi-regime in ICE hybrid vehicle (ICEHV) modelling is introduced to describe designs that allow for multiple configurations and operating regimes. A dynamic mathematical model of a power-split architecture with two modes (or configurations) introduced by General Motors Corporation is developed and a steady-state version is programmed into the ADvanced VehIcle SimulatOR (ADVISOR) simulation software package. This ADVISOR model is applied to a commercial delivery vehicle, and the fuel consumption of

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the vehicle undergoing a variety of drive cycles is determined. The two-mode model is compared to the ADVISOR models for the Toyota Hybrid System (THS), parallel hybrid, and conventional powertrains in the same vehicle. The results show that for this vehicle type, the two-mode design achieves lower fuel consumption than the THS and conventional powertrains, and only slighter greater fuel consumption than the parallel hybrid design. There is also considerable potential for improvement in performance of the two-mode model through the development of an optimal power management strategy.

In the medium- to long-term, the necessity for zero-emission vehicles may position fuel cell systems (FCSs) to be commercialized as on-board energy conversion devices. FCSs are currently inordinately expensive with power density and durability issues, among other design problems. Fuel cell hybrid vehicle (FCHV) designers must use the available design techniques intelligently to overcome the limitations and take advantage of the higher efficiency capabilities of the fuel cell. As the first step in creating a virtual prototype of a FCS, a semi-empirical model of the system is developed and further enhancements such as transient response modelling are proposed. An optimization of the operating parameters to maximize average net power and average exergetic efficiency is conducted, and the technique is applied to the FCS model for the prototype fuel cell hybrid scooter (FCHS). The optimizations demonstrate that significant improvements in performance can be achieved, and that optimizations with more design variables are warranted.

Models of a conventional battery scooter (BS) and of the FCHS are developed in ADVISOR. Simulations are conducted which compare the performance of the two models. Subsequently, performance tests of the BS and FCHS are conducted using a chassis dynamometer. Despite problems with the prototype FCHS, the tests confirm the theoretical results: the FCHS model achieves higher performance in terms of acceleration and power, while the BS model operates more efficiently and requires less energy.

This study provides better understanding on the emerging FCHV and ICEHV technologies; introduced new and improved models for FCHV and multi-regime hybrid powertrains;

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developed FCHV and ICEHV performance simulation and design optimization methods using the new computer models; explored the methods for validating the computer models using prototype BS and FCHS on a research dynamometer; identified areas of improvements of the new experiment methods; and formed the foundation for future research in related areas.

vi

Table of Contents

Supervisory Committee: (ii)

Abstract (iii)

Table of Contents (vi)

List of Figures (x)

List of Tables (xiv)

List of Abbreviations (xvi)

Acknowledgements (xxi)

Frontispiece (xxii)

Chapter 1Background and Motivation (1)

1.1 Environmental

Concerns (1)

1.2 Resource Supply and Energy Efficiency (4)

1.3 Contemporary Vehicle Powertrain Technologies (5)

1.4 Internal Combustion Engine Vehicles (6)

Vehicles (12)

1.5 Electric

1.5.1 Fuel cell vehicles (15)

Vehicles (19)

1.6 Hybrid

1.6.1 Hybrid vehicle history (22)

1.6.2 Fuel cell hybrid vehicles (25)

1.6.3 Commercialization barriers to fuel cell hybrid vehicles (26)

1.6.4 Fuel cell hybrid vehicle industry status (28)

1.6.5 Fuel cell low-speed vehicles (30)

Focus (33)

1.7 Research

1.8 Research

Tools (36)

Chapter 2State of the Art of Commercialized Hybrid Vehicles (38)

2.1 Categories of Internal Combustion Engine Hybrid Vehicles (38)

2.1.1 Series architecture (41)

2.1.2 Parallel architecture (43)

2.1.3 Power-split architecture (45)

2.1.4 One-mode, two-mode and multi-regime powertrain architectures (49)

2.2 General Motors Designs (51)

Design (61)

2.3 Renault

Design (62)

2.4 Timken

Design (64)

2.5 Silvatech

2.6 University of Michigan-Dearborn Design (66)

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Chapter 3Multi-Regime Powertrain Architecture Modelling (69)

Multi-regime

Powertrain Architecture Modelling (69)

3.1 Previous

3.2 Study Multi-Regime Powertrain Architecture Model (71)

3.2.1 Mechanical path (74)

3.2.2 Node points (85)

3.2.3 Electrical path (86)

3.2.4 Power management strategy (88)

3.2.5 Vehicle models (94)

3.2.6 Simulations (96)

3.2.7 Study results and discussion (98)

Chapter 4PEM Fuel Cell and Stack Modelling (104)

4.1 Previous Fuel Cell Modelling (104)

4.2 PEM Fuel Cell and Stack Model (110)

4.2.1 PEM fuel cell fundamentals (111)

4.2.2 Theoretical PEM fuel cell potential (113)

4.2.3 Nernst voltage (114)

4.2.4 PEM fuel cell terminal voltage (117)

4.2.5 Activation overvoltage (119)

4.2.6 Ohmic overvoltage (120)

4.2.7 Concentration overvoltage (122)

4.3 Proposed Modifications to the Generalized Electrochemical Steady-State

Degradation Model (123)

4.3.1 Fuel crossover and internal currents (124)

4.3.2 Contact resistance (125)

4.3.3 Electrochemical dynamic effects (126)

4.3.4 Membrane hydration model (129)

4.3.5 Anode flow module (132)

4.3.6 Cathode flow module (134)

Chapter 5Fuel Cell System Modelling (140)

5.1 Previous Fuel Cell System Modelling (140)

5.2 Fuel Cell System Model (141)

5.3 Oxidant Flow Module (143)

5.3.1 Compressor module (143)

5.3.2 Supply manifold module (147)

5.3.3 Return manifold (149)

5.3.4 Oxidant humidifier module (150)

5.4 Hydrogen Flow Module (156)

5.5 Thermal Management Module (162)

5.5.1 Passive heat loss (163)

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5.5.2 Active heat loss (165)

5.6 Fuel Cell System Net Output (167)

5.6.1 Efficiency analysis (168)

Chapter 6Low-Speed Vehicle Modelling (171)

6.1 Previous Fuel Cell Hybrid Scooter Modelling (172)

Models (173)

Vehicle

6.2 Low-Speed

6.2.1 Vehicle body (173)

6.2.2 Electric machine (176)

6.2.3 Fuel cell hybrid scooter powertrain model (179)

6.2.4 Battery models (181)

6.2.5 Control strategy models (183)

Simulations (183)

6.3 LSV

6.3.1 Acceleration simulation (184)

6.3.2 Gradeability simulation (186)

6.3.3 New York City Cycle simulation (187)

6.3.4 Step input drive cycle simulation (189)

6.3.5 Range simulation (190)

Chapter 7Low-Speed Vehicle Testing (192)

Relationships (196)

7.1 Dynamometer

Calibration (199)

7.2 Dynamometer

Results (203)

7.3 Experimental

7.3.1 Dynamometer gradeability test (203)

7.3.2 Dynamometer step-input test (206)

7.3.3 Dynamometer range test (211)

Chapter 8Fuel Cell System Optimization (213)

8.1 Previous Fuel Cell-Related Optimization (213)

8.1.1 Fuel cell optimization (214)

8.1.2 Fuel cell stack optimization (214)

8.1.3 Fuel cell system optimization (215)

8.2 Study optimization of a fuel cell system (216)

8.2.1 Multi-objective optimization (218)

8.2.2 Single-objective optimization problem (225)

8.3 Study Optimization of a Fuel Cell Hybrid Scooter Powertrain (232)

Chapter 9Conclusions and Outlook (238)

9.1 Internal combustion engine hybrid vehicle powertrains (238)

9.2 Fuel cell system model (241)

models (243)

9.3 LSV

testing (245)

9.4 Dynamometer

ix 9.5 Fuel cell hybrid scooter design (246)

tools (248)

9.6 Design

9.6.1 Simulation software (248)

9.6.2 Computer-aided design model (249)

9.6.3 Mathematical optimization (250)

9.7 Hybrid vehicle outlook (251)

References (254)

Appendix A: Vehicle Classifications (270)

Appendix B: Stack Degradation (272)

Appendix C: Optimization Algorithms (278)

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List of Figures

Figure 1-1. Current vehicle powertrain technology categories (6)

Figure 1-2. Changes in internal combustion engine vehicles 1975-2007 (11)

Figure 1-3. Fuel cell stack (a) and system (b) cost break-down (17)

Figure 1-4. The Lohner-Porsche Mixte (23)

Figure 2-1. Types of internal combustion engine hybrid vehicles (40)

Figure 2-2. Configuration of a generic series hybrid vehicle (42)

Figure 2-3. Configuration of a generic parallel hybrid vehicle (44)

Figure 2-4. Configuration of a parallel hybrid four wheel drive (45)

Figure 2-5. Illustration of a planetary gear (52)

Figure 2-6. GM-1 architecture (53)

Figure 2-7. GM-2 architecture (56)

Figure 2-8. Two-Mode EVT architecture (57)

Figure 2-9. Two-Mode Hybrid architecture (60)

Figure 2-10. Renault IVT architecture (61)

Figure 2-11. Timken eCVT architecture (63)

Figure 2-12. Silvatech EMCVT architecture (65)

Figure 2-13. University of Michigan-Dearborn architecture (67)

Figure 3-1. Architecture of the study multi-regime powertrain (72)

Figure 3-2. Free-body diagram of the first planet gear (77)

Figure 3-3. Free-body diagram of the first sun gear (79)

Figure 3-4. Free-body diagram of the first ring gear (80)

Figure 3-5. Free-body diagram of rigid body of carrier gears, intermediate and drive shafts (82)

Figure 3-6. Schematic representation of backward-looking structure model (89)

Figure 3-7. Schematic representation of forward-looking structure model (89)

Figure 3-8. Schematic of the study multi-regime architecture control strategy (93)

Figure 3-9. Sample N/V plot for the study multi-regime architecture (94)

xi

Figure 3-10. ICEHV Simulation drive cycles (98)

Figure 3-11. ICEHV simulation results: fuel consumption for the unloaded case (99)

Figure 3-12. ICEHV simulation results: fuel consumption for the loaded case (100)

Figure 3-13. ICEHV simulation results: engine power of the multi-regime powertrain during the City-Suburban Heavy Vehicle Route cycle (101)

Figure 3-14. ICEHV simulation results: MG1 power of the multi-regime powertrain during the City-Suburban Heavy Vehicle Route cycle (101)

Figure 3-15. ICEHV simulation results: MG2 power of the multi-regime powertrain during the City-Suburban Heavy Vehicle Route cycle (102)

Figure 3-16. ICEHV simulation results: ESS SOC history of the multi-regime powertrain during the City-Suburban Heavy Vehicle Route cycle (102)

Figure 4-1. Reactions of a PEMFC (112)

Figure 4-2. Polarization curve of a PEMFC (118)

Figure 4-3. Fuel cell equivalent RC circuit (127)

Figure 5-1. Fuel cell system schematic (142)

Figure 5-2. Oxidant humidifier schematic (151)

Figure 5-3. Fuel cell system schematic with hydrogen re-circulation (161)

Figure 6-1. Scooter tire and rim dimensions (175)

Figure 6-2. Three-dimensional motor efficiency map estimate (178)

Figure 6-3. Contour plot of the motor efficiency map estimate (179)

Figure 6-4. Schematic of the prototype fuel cell hybrid scooter powertrain (180)

Figure 6-5. Internal resistor battery model (181)

Figure 6-6. New York City Cycle drive cycle (188)

Figure 7-1. Dynamometer components (194)

Figure 7-2. Prototype low-speed vehicles and the fuel cell system of the fuel cell hybrid scooter (194)

Figure 7-3. Test screen of the dynamometer computer software (195)

xii

Figure 7-4. Measured dynamometer friction torque versus roller speed (199)

Figure 7-5. Scooter wheel friction torques versus roller speed (201)

Figure 7-6. Required current and calculated torque versus vehicle velocity (203)

Figure 7-7. Torque-speed curve from dynamometer gradeability results (205)

Figure 7-8. Peak roller speeds achieved by the LSVs during the step-input dynamometer tests (207)

Figure 7-9. LSV motor torque values at peak speed for the step-input tests (208)

Figure 7-10. LSV motor power values at peak speed for the step-input tests (209)

Figure 7-11. LSV motor torque values at peak acceleration for the step-input tests (210)

Figure 7-12. LSV motor power values at peak acceleration for the step-input tests (211)

Figure 8-1. Flow diagram of optimization methodology (218)

Figure 8-2. Net Power curve for the non-weighted, multi-objective problem: average net power (221)

Figure 8-3. Gross power curve for the non-weighted, multi-objective problem: average net power (222)

Figure 8-4. Air compressor power curve for the non-weighted, multi-objective problem: average net power (223)

Figure 8-5. System exergetic efficiency curve for the non-weighted, multi-objective

problem: average net power (224)

Figure 8-6. System exergetic efficiency curve for the non-weighted, multi-objective

problem: average system exergetic efficiency (225)

Figure 8-7. Net power curve for the single-objective problem: peak net power (SQP-1 algorithm) (228)

Figure 8-8. Gross power curve for the single-objective problem: peak net power (SQP-1 algorithm) (228)

Figure 8-9. Net power curve for the single-objective problem: peak net power (global optimization and SQP-2 algorithms) (229)

xiii

Figure 8-10. Gross power curve for the single-objective problem: peak net power (global optimization and SQP-2 algorithms) (230)

Figure 8-11. Air compressor power curve for the single-objective problem: peak net power (global optimization and SQP-2 algorithms) (230)

Figure 8-12. System exergetic efficiency curve for the single-objective problem: peak system exergetic efficiency (232)

Figure 8-13. New York City Cycle fuel cell system power request distribution (234)

Figure B-1. Catalytic activity term versus age (274)

Figure B-2. Internal resistance and semi-empirical membrane parameter versus age (275)

Figure B-3. V oltage degradation model versus experimental results (277)

Figure C-1. Flowchart for the GA process (281)

Figure C-2. Illustration of the crossover process (283)

xiv

List of Tables

Table 1-1. Sample internal combustion engine vehicle emission improvements (10)

Table 1-2. Battery and ultracapacitor technology comparison (14)

Table 1-3. Fuel cell industry reported results and U.S. Department of Energy targets (18)

Table 1-4. Commercial battery scooters (31)

Table 1-5. Fuel cell hybrid scooters (32)

Table 2-1. Classification system of the California Air Resources Board for hybrid vehicles (39)

Table 2-2. Power-split architecture operation (48)

Table 2-3. Operating regimes of the GM-1 architecture (54)

Table 2-4. Operating regimes of the GM-2 architecture (56)

Table 2-5. Operating regimes of the Two-Mode EVT architecture (58)

Table 2-6. Operating regimes of the Two-Mode Hybrid architecture (60)

Table 2-7. Operating regimes of the Renault architecture (62)

Table 2-8. Operating regimes of the Timken architecture (64)

Table 2-9. Operating regimes of the Silvatech architecture (66)

Table 2-10. Operating regimes of University of Michigan-Dearborn architecture (68)

Table 3-1. ICEHV study model operating regimes (74)

Table 3-2. Characteristics of the ICEHV study vehicle (95)

Table 3-3. Powertrain components of the ICEHV simulation architectures (96)

Table 4-1. Activation overvoltage coefficients (120)

Table 5-1. Compressor regression coefficients (147)

Table 6-1. Acceleration simulation results for the battery scooter model (185)

Table 6-2. Acceleration simulation results for the fuel cell hybrid scooter model (186)

Table 6-3. Gradeability simulation results for the LSV models (187)

Table 6-4. New York City Cycle simulation results for the LSV models (188)

Table 6-5. Step input simulation results for the LSV models (189)

Table 6-6. Range simulation results for the LSV models (190)

xv

Table 7-1. Results of the dynamometer and simulation gradeability tests (204)

Table 7-2. Motor torque values for the dynamometer gradeability test (205)

Table 7-3. Comparison of the power at the wheels and output by the motor (206)

Table 7-4. Battery scooter range test pulse-width modulation changes (212)

Table 8-1. Solution to the non-weighted multi-objective problem: average net power (220)

Table 8-2. Solution of the non-weighted multi-objective problem: average exergetic

efficiency (224)

Table 8-3. Solution to the single-objective problem: peak net power (226)

Table 8-4. Solution to the single-objective problem: peak system exergetic efficiency (231)

Table 8-5. Power request categories (234)

Table 8-6. Optimization solutions for weighted, multi-objective problems, 39 cells (235)

Table 8-7. Solutions of the weighted, multi-objective problem: average exergetic efficiency (236)

Table A-1. Classification and acronym summary (270)

xvi

List of Abbreviations

ADVISOR: ADvanced VehIcle SimulatOR

AEE: advanced engineering environment

AER: all-electric

regime

AFC: alkaline fuel cell

AGCC: anthropogenic global climate change

ANN: artificial neural network

ANL: Argonne National Laboratory

ARSM: Adaptive Response Surface Method

vehicle

BV: battery

BLDC: brushless direct current

BOP: balance of plant

CAC: criteria air contaminant

CAD/CAM/CAE computer aided drafting/manufacturing/engineering

CAFE: Corporate Automobile Fuel Economy

CARB: California Air Resources Board

cc: cubic

centimetre

CCD: Central Composite Design

CFCD: computational fuel cell dynamics

CFD: computational fluid dynamics

CHP: combined heat and power

monoxide

CO: carbon

dioxide

CO2: carbon

CO2e: carbon dioxide equivalent

Year

Life

Adjusted

DALY: Disability

current

DC: direct

RECTangles

DIRECT: DIviding

xvii

DoE: Department of Energy

DOH: degree of hybridization

DPF: diesel particulate filter

DPM: diesel particulate matter

ECU: electronic control unit

machine

EM: electric

force

EMF: electromotive

ESS: energy storage system

vehicle

EV: electric

EVT: electronically variable transmission

FCB: fuel cell bus

FCHV: fuel cell-hybrid vehicle

FCHS: fuel cell-hybrid scooter

cell

system

FCS: fuel

FCV: fuel cell vehicle

FEA: finite element analysis

Algorithms

GA: Genetic

GCM: Global Circulation Model

GCTool: Generalized

Toolkit

Computational

GDL: Gas Diffusion Layer

Electrochemical Steady-State Degradation Model GESSDM: Generalized

GHG: greenhouse

gas

Motors

GM: General

optimization

GO: global

HC: hydrocarbon

HV: hybrid electric vehicle

HHV: higher heating value

xviii

HY-ZEM: Hybrid-Zero Emission Mobility

ICE: internal combustion engine

ICEHV: internal combustion engine-hybrid vehicle

ICES: internal combustion engine scooter

IEC: International Electrotechnical Commission

IESVic: Integrated Energy Systems at the University of Victoria

IGEC: International Green Energy Conference

ISG: integrated

starter-generator

ITRI: Industrial Technology Research Institute of Taiwan

L-A: lead-acid

LCA: life-cycle

analysis

LHV: lower heating value

LION: Lithium-Ion

LSVTF: Low-Speed Vehicle Testing Facility

vehicle

LSV: low-speed

assembly

MEA: membrane-electrode

hydride

MH: metal

MOI: moment of inertia

MPSM: Mode-Pursuing Sampling Method

NASA: National Aeronautics and Space Administration

NEDC: New European Drive Cycle

NiMH: nickel metal hydride

NO x: Nitrogen oxides (various)

NREL: National Renewable Energy Laboratory

NYCC: New York City Cycle

voltage

OCV: open-circuit

OEM: original equipment manufacturer

xix

PEMFC: proton exchange membrane fuel cell

PHV: plug-in hybrid electric vehicle

PM: permanent

magnet

request

PR: power

oxidation

PROX: preferential

PSAT: Powertrain Systems Analysis Tool

PTL: Porous Transport Layer

modulation

PWM: pulse-width

RMC: Royal Military College

Annealing

SA: Simulated

ignition

SI: spark

SOC: state of charge

reformer

SMR: steam

methane

Quadratic

Programming

SQP: Sequential

steady-flow

SSSF: steady-state,

Temperature

and Pressure (298.15K, 101.325 Pa)

STP: Standard

External

manifolding, and Radiator Stack

TERS: Tri-stream,

TMDC: Taipei Motorcycle Drive Cycle

vehicle

UCV: ultracapacitor

USD United States Dollar

manufacturing

VM: virtual

VOC: volatile organic compound

VP: virtual

prototyping

WGS: water gas shift

WHO: World Health Organization

WOT: wide-open

throttle

management

thermal

WTM: water

and

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vehicle

ZEV: zero-emission

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Acknowledgements

There are many people I would like to thank for their help and encouragement throughout my time here at the University of Victoria. I will confine myself to people who had direct involvement with my degree and not list the many other people in my life that are important to me and make life so enjoyable: you know who you are.

Dr. Zuomin Dong, my supervisor, is given special acknowledgement for the mentoring and many enlightening discussions we had about fuel cells, hybrid vehicles, and anything else we thought would be interesting.

The work done by fellow (and former) graduate students Leon Zhou, Matthew Guenther, Sezer Tezcan, Richard Stackhouse, and Michael Pastula contributed immensely to my work.

Dr. Andrew Rowe was always there to help me with my myriad of questions on any number of topics from hydrogen storage technologies to the amount of weight that is reasonable to lift at the gym.

Dr. Peter Wild was able to give very useful advice, no matter what the topic, and his course on renewable energy was certainly an eye-opener for me.

Dr. Lawrence Pitt is one of the most knowledgeable people I have ever met, and I learned so much from him during our discussions.

Dr. Nedjib Djilali helped with my professional development in the area of fuel cells.

Dr. Henning Struchtup was supportive in my efforts to understand exergy.

Susan Walton, Peggy White and Erin Robinson were tireless in their administrative efforts.

My crack editing team, consisting of Dr. Thomas (Pops) Wishart, Jacquie and Wynn Downing, Victoria Wishart, Marc Secanell, Richard (DB) Humphries and Amber McGown, graciously and courageously helped me complete this tome.

Fellow students, in (approximate) order that I met you: Christina Ianniciello, David Brodrecht, Jeffrey Coleman, Aimy Bazylak, Matthew Schuett, Juan Mejia, Ramadan Abdiwe, Michael Carl, Michael Optis, James Biggar and the members of Common Energy. You have all helped confirm that life in graduate studies is not only about the degree.

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