Modeling and Control of Hybrid Propulsion System for Ground Vehicles (eBook)

Yuan Zou, Junqiu Li, Xiaosong Hu, Yann Chamaillard (Autoren)

eBook Download: PDF

2018 | 1st ed. 2018
IX, 328 Seiten
Springer Berlin Heidelberg (Verlag)
978-3-662-53673-5 (ISBN)

Infos

This book focuses on the systematic design of architectures, parameters and control of typical hybrid propulsion systems for wheeled and tracked vehicles based on a combination of theoretical research and engineering practice. Adopting a mechatronic system dynamics perspective, principles and methods from the fields of optimal control and system optimization are applied in order to analyze the hybrid propulsion configuration and controller design. Case investigations for typical hybrid propulsion systems of wheeled and tracked ground vehicles are also provided.

Inhalt

Contents 5
1 Introduction 10
1.1 Current Situation of the Ground Vehicle Hybrid Drive System 10
1.1.1 The Development History of Ground Vehicle Propulsion Systems 10
1.1.2 The Current Situation and Development of Ground Vehicle Hybrid Drive Systems 12
1.1.3 The Development and Technical Features of Ground Vehicle Hybrid Drive Systems 16
1.2 Hybrid Drive System Control Technology of Ground Vehicles 18
1.2.1 Role of System Control in Hybrid Drive Systems 18
1.2.2 Control Structures of Hybrid Drive System 18
1.3 Model-Based System and Control Optimization 21
1.3.1 Model-Based Control 21
1.3.2 The System Optimization of Ground Vehicle Hybrid Drive Systems 23
1.3.3 Optimal Control of Hybrid Drive System 25
References 28
2 Architecture of the Ground Vehicle Hybrid Drive System 31
2.1 Basic Architecture and Classification of the Hybrid Drive System 31
2.1.1 Basic Architecture of the Hybrid Drive System 31
2.1.2 Classification of the Hybrid Drive System 34
2.1.2.1 Classification Based on Energy Composition Method 34
2.1.2.2 Classification Based on the Structure of Hybrid Drive System 37
2.1.2.3 Classification Based on DOH 40
2.2 Hybrid Drive System for Wheeled Vehicles 41
2.2.1 Serial Hybrid Drive System 41
2.2.2 Parallel Hybrid Drive System 43
2.2.3 Serial–Parallel Hybrid Drive System 46
2.3 Hybrid Drive System for Tracked Vehicle 50
2.3.1 Series Hybrid Drive System 50
2.3.2 Parallel and Serial–Parallel Hybrid Drive System 52
References 58
3 Modeling and Simulation Technology for Ground Vehicle Hybrid Propulsion System 60
3.1 The Challenge of the Modeling and Simulation of a Hybrid Powertrain System 60
3.2 Models of a Ground Vehicle and Hybrid Powertrain System 63
3.2.1 The Vehicle Dynamics Model 63
3.2.1.1 The Wheeled Vehicle Dynamics Model 63
3.2.1.2 The Tracked Vehicle Dynamics Model 64
3.2.2 Engine Model 71
3.2.3 Transmission System Model 74
3.2.4 Energy Storage Model 76
3.2.4.1 Lithium-ion Battery [10] 76
3.2.4.2 Fuel Cell Model 78
3.2.4.3 Supercapacitor 83
3.2.4.4 Flywheel 84
3.2.4.5 Hydraulic Accumulator 85
3.2.5 Motor System Model 86
3.2.5.1 Electromechanical Conversion Static Model 86
3.2.5.2 DC Motor System Model 87
3.2.5.3 The AC Motor System Model [22, 25] 90
3.2.6 Electric Power Bus and Power Converter Model 93
3.2.6.1 Electric Power Bus Model 93
3.2.6.2 DC–DC Converter Model [26, 27] 94
3.3 Ground Vehicle Hybrid Powertrain System Simulation Technology 96
3.3.1 Control-oriented System Simulation Technology 96
3.3.1.1 Forward Simulation and Backward Simulation 96
3.3.1.2 Software-In-Loop Simulation (SIL), Hardware-In-Loop Simulation (HIL), and Component-In-Loop Simulation (CIL) 98
3.3.2 Simulation Software and Environment 99
3.3.2.1 ADVISOR Software [29] 100
3.3.2.2 AUTONOMIE Software [30] 102
3.3.2.3 CRUISE Software [31] 102
3.3.2.4 Other Electromechanical System Software for Ground Vehicle Hybrid Powertrain System Simulation 103
References 103
4 The Modeling and Identification of Lithium-Ion Battery System 105
4.1 The Categories and Comparison of Vehicle Power Battery 105
4.2 The Categories and Comparison of Vehicle Lithium-Ion Battery 106
4.3 The Categories of Models of Lithium-Ion Batteries 108
4.3.1 Electrochemical Model 108
4.3.2 Black-Box Battery Model 109
4.3.3 Equivalent Circuit Model 109
4.4 The Application of Lithium-Ion Battery Model in Vehicle-Level Simulation and Battery Management 110
4.4.1 The Application of Lithium-Ion Battery Model in Vehicle Energy Management Strategy Simulation 110
4.4.2 The Application of Lithium-Ion Battery Model in Battery Management 111
4.5 The Identification Methods of Lithium-Ion Battery Model 113
4.6 Optimal Estimation Methods of Lithium-Ion Battery States 114
4.6.1 Filter Coefficients and Adjustment 115
4.6.2 Extended Kalman Filter 115
4.7 Case Studies 118
4.7.1 The Linear Battery Model Identification Based on Least Square Algorithm [63, 64] 118
4.7.2 The Nonlinear Battery Model Identification Based on Numerical Optimization [57] 123
4.7.3 Optimal Kalman Filter-Based SOC and SOH Estimation of Lithium-Ion Battery 126
4.7.3.1 Optimal Estimation of Lithium-Ion Battery SOC 126
4.7.3.2 Combined SOC and SOH Estimation 132
References 143
5 Optimal Control and System Optimization of Ground Vehicle Hybrid Drive System 147
5.1 Mathematic Fundamental of Ground Vehicle Hybrid Drive System Optimal Control 148
5.1.1 Deterministic Dynamic Programming Theory and Fundamental 150
5.1.1.1 Principle of Optimality 150
5.1.1.2 Recurrence Equation of Dynamic Programming 151
5.1.1.3 Dynamic Programming Algorithm 153
5.1.2 Stochastic Dynamic Programming Theory and Fundamental 154
5.1.3 Pontryagin’s Minimum Principle Fundamental [11] 156
5.2 Optimal Control of Parallel Hybrid Commercial Vehicle Based on Deterministic Dynamic Programming 159
5.2.1 Vehicle Structure and Its Component Modeling 159
5.2.1.1 Vehicle Structure and Main Parameters 159
5.2.1.2 Hybrid Drive System Modeling 160
5.2.2 Static Optimization-Based Control Design 167
5.2.3 Optimal Energy Management for Hybrid Electric Vehicle 169
5.2.3.1 Formulation and Solution of Dynamic Programming Problem 169
5.2.3.2 Results 171
5.2.3.3 Control Rule Extraction and Suboptimal Control Strategy 171
5.2.3.4 Dynamic Programming Improved Control Strategy Simulation Verification 177
5.2.4 Conclusion 180
5.3 Pontryagin’s Minimum Principle-Based Energy Management for a Parallel Hybrid Electric Vehicle [17] 180
5.3.1 Problem Formulation 180
5.3.2 PMP-Based Results 183
5.4 Optimal Control Based on Stochastic Dynamic Programming [18, 19] 185
5.4.1 Hybrid Tracked Vehicle Powertrain and Modeling 188
5.4.2 SDP-Based Optimal Control Design 190
5.4.2.1 Power Demand and Power Transition Probability 190
5.4.2.2 SDP Solution 192
5.4.3 Results Discussion and Conclusions 195
5.5 Combined Optimal Design for System Parameters and Control 198
5.5.1 Coupled Optimization of System Parameter and Control 198
5.5.2 Combined Parameter and Control Optimization Based on Optimal Control Theory 199
5.5.2.1 Theoretical Fundamentals 199
5.5.2.2 Combined Optimal Design for Tracked Vehicle Hybrid Drive System Parameter and Control [29] 201
References 208
6 The Nonlinear Programming Optimal Control of a Hybrid Drive System 210
6.1 The Conversion of the Optimal Control Problem to the Nonlinear Programming Problem 210
6.1.1 The Indirect Method 211
6.1.2 The Direct Method 212
6.2 The Theoretical Basis of Pseudo-Spectral Method 213
6.2.1 Discretization of State and Control Variables 215
6.2.2 Differential Matrix and Derivative Approximation 216
6.2.3 The Solution to the NLP Problem 217
6.3 The Solution to A Hybrid Vehicle Optimal Control Problem 218
6.3.1 The Vehicle Model and Problem Formulation 218
6.3.2 The Result Analysis and Comparison 220
6.4 Convex Optimization Fundamental 223
6.4.1 The Significance and Advantages of Convex Optimization 223
6.4.2 Convex Optimization Concept 224
6.5 Dimensioning and Power Management of the Hybrid Energy Storage System in a Fuel Cell Hybrid Electric Bus 225
6.5.1 Introduction 226
6.5.2 Modeling of Fuel Cell Hybrid Bus Powertrain 226
6.5.3 Battery SOH Model 232
6.5.4 Convex Optimization Framework for HESS Sizing and Energy Management 235
6.5.5 Optimization Results with Different Replacement Strategies 238
6.5.5.1 Optimal Battery Replacement Strategy 238
6.5.5.2 Comparison with Battery-Only ESS Option 239
6.5.6 Comparison with Optimization Scenario Neglecting Battery SOH 245
6.5.7 Further Discussion 246
References 248
7 Application of Hybrid Drive System Modeling and Control for Wheeled Vehicles 251
7.1 Optimal Control of Power-Split Hybrid Drive System 251
7.1.1 Hybrid Drive System Model 252
7.1.1.1 Planetary Gear Model 252
7.1.1.2 Engine Model 254
7.1.1.3 Motor/Generator Model 254
7.1.1.4 Battery Model 254
7.1.1.5 Powertrain Model 255
7.1.2 Optimal Control of Power-Split Hybrid Drive System 257
7.1.3 Results and Discussion 259
7.2 Real-Time Simulation of Parallel Hybrid Vehicle Commercial Control 265
7.2.1 dSPACE-Based Hardware-In-Loop (HIL) Simulation 265
7.2.2 Hybrid Commercial Vehicle “Driver–Vehicle Control Unit” in Loop Real-Time Simulation Platform 266
7.2.2.1 The Structure of the Real-Time Simulation Platform 266
7.2.2.2 Algorithm Code Generation in the Vehicle Control Unit 268
7.2.2.3 CAN Bus Communication and the System Model in dSPACE 269
7.2.3 Real-Time Simulation of Hybrid Commercial Vehicle “Driver–Vehicle Control Unit” In-loop 270
References 273
8 Application of Hybrid Drive System Modeling and Control for Tracked Vehicles 274
8.1 Modeling and Control for Hybrid High-Speed Tracked Vehicle 274
8.1.1 Parameter Matching of Hybrid Drive System for the High-Speed Dual-motor Drive Tracked Vehicle 275
8.1.1.1 Matching Design for Hybrid Propulsion Dual-Motor Drive Tracked Vehicles 275
8.1.1.2 Coupling of Engine-Generator and Battery Without the DC–DC Converter 282
8.1.1.3 Coupling of Engine-Generator Set and Battery Pack with DC–DC Converter 284
8.1.2 Control Strategy Design for the High-Speed Tracked Vehicle Driven by Dual Independent Motors 284
8.1.2.1 Tracked Vehicle Driving Control [7] 284
8.1.2.2 Energy Management and Control 301
8.2 A Case Study: The Hybrid-Tracked Bulldozer 309
8.2.1 Modeling for Driving System of Hybrid-Tracked Bulldozer and Parameter Matching 309
8.2.1.1 The Matching Design of the Two-Motor Driving System for a Hybrid Bulldozer 310
8.2.1.2 Parameters Matching for Engine-Generator Set and Super Capacitor 313
8.2.1.3 Matching and Validation of Engine-Generator and Super Capacitor Based on Dynamic Programming (DP) 315
8.2.2 Control Design for Hybrid Bulldozer [16] 320
8.2.2.1 Control Scheme 320
8.2.2.2 Engine-Generator Control 321
8.2.2.3 Speed Control of Dual Motor Drives 324
8.2.3 Rapid Control Simulation Engineering for a Hybrid Bulldozer 325
8.2.3.1 Simulation Model and Platform 325
8.2.3.2 Control Program and Controlled Plant Model Loading 327
8.2.3.3 Test Results 328
References 330

Produkt-Details

• Erscheint lt. Verlag: 2.7.2018
• Zusatzinfo: IX, 328 p. 238 illus., 122 illus. in color.
• Verlagsort: Berlin
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Naturwissenschaften
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Fahrzeugbau / Schiffbau
• Themenwelt: Technik ► Maschinenbau
• Schlagworte: Ground Vehicle • Hybrid Powertrain • hybrid propulsion • hybrid vehicle • Modeling • optimal control • System Optimization
• ISBN-10: 3-662-53673-0 / 3662536730
• ISBN-13: 978-3-662-53673-5 / 9783662536735