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Cyber-Physical Vehicle Systems: Methodology and Applications
Chen Lv, Yang Xing, Junzhi Zhang, and Dongpu Cao
www.morganclaypool.com
ISBN: 9781681737317 paperback
ISBN: 9781681737324 ebook
ISBN: 9781681737331 hardcover
DOI 10.2200/S00969ED1V01Y201912AAT010
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON ADVANCES IN AUTOMOTIVE TECHNOLOGIES
Lecture #10
Series Editor: Amir Khajepour, University of Waterloo
Series ISSN
Print 2576-8107 Electronic 2576-8131
Cyber-Physical Vehicle Systems
Methodology and Applications
Chen Lv
Nanyang Technological University, Singapore
Yang Xing
Nanyang Technological University, Singapore
Junzhi Zhang
Tsinghua University, P.R. China
Dongpu Cao
University of Waterloo
SYNTHESIS LECTURES ON ADVANCES IN AUTOMOTIVE
TECHNOLOGIES #10
C
M
&
cLaypoolMorgan publishers
&
ABSTRACT
is book studies the design optimization, state estimation, and advanced control methods for
cyber-physical vehicle systems (CPVS) and their applications in real-world automotive systems.
First, in Chapter 1, key challenges and state-of-the-art of vehicle design and control in the con-
text of cyber-physical systems are introduced. In Chapter 2, a cyber-physical system (CPS) based
framework is proposed for high-level co-design optimization of the plant and controller param-
eters for CPVS, in view of vehicle’s dynamic performance, drivability, and energy along with
different driving styles. System description, requirements, constraints, optimization objectives,
and methodology are investigated. In Chapter 3, an Artificial-Neural-Network-based estima-
tion method is studied for accurate state estimation of CPVS. In Chapter 4, a high-precision
controller is designed for a safety-critical CPVS. e detailed control synthesis and experimen-
tal validation are presented. e application results presented throughout the book validate the
feasibility and effectiveness of the proposed theoretical methods of design, estimation, control,
and optimization for cyber-physical vehicle systems.
KEYWORDS
cyber-physical vehicle systems, co-design optimization, dynamic modeling, design
space exploration, parameter optimization, state estimation, neural networks, con-
troller synthesis, simulation validation, experimental testing
vii
Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
1
Introductions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2
Co-Design Optimization for Cyber-Physical Vehicle System . . . . . . . . . . . . . . . 5
2.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Hierarchical Optimization Methodology . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.3 Driving Event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.4 Driving Style Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.5 Requirements for the Design and Optimization of CPVS . . . . . . . . . . 9
2.1.6 Constraints for Vehicle Design and Optimization . . . . . . . . . . . . . . . 10
2.2 System Modeling and Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 Electric Powertrain system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.2 Blended Brake System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.3 Dynamic Model of the Vehicle and Tyre . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.4 Experimental Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Controller Design for Different Driving Styles . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.1 High-Level Controller Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.2 Low-Level Controller for Different Driving Styles . . . . . . . . . . . . . . 14
2.4 Driving-Style-Based Performance Exploration and Parameter
Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.1 Design Space Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.2 Performance Exploration Methodology . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.3 Driving-Style-Oriented Multi-Objective Optimization . . . . . . . . . . . 16
2.5 Optimization Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5.1 Optimization Results for the Aggressive Driving Style . . . . . . . . . . . . 19
2.5.2 Optimization Results of the Moderate Driving Style . . . . . . . . . . . . . 19
2.5.3 Optimization Results of the Conservative Driving Style . . . . . . . . . . 21
2.5.4 Comparison and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
viii
3
State Estimation of Cyber-Physical Vehicle Systems . . . . . . . . . . . . . . . . . . . . . 23
3.1 Multilayer Artificial Neural Networks Architecture . . . . . . . . . . . . . . . . . . . . . 25
3.1.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1.2 Multilayer Feed-Forward Neural Network . . . . . . . . . . . . . . . . . . . . . 25
3.2 Standard Backpropagation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3 Levenberg–Marquardt Backpropagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4 Experimental Testing and Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4.1 Testing Vehicle and Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4.2 Data Collection and Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4.3 Feature Selection and Model Training . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.5 Experiment Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.5.1 Results of the ANN-Based Braking Pressure Estimation . . . . . . . . . . 38
3.5.2 Importance Analysis of the Selected Features . . . . . . . . . . . . . . . . . . . 40
3.5.3 Comparison of Estimation Results with Different Learning
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4
Controller Design of Cyber-Physical Vehicle Systems . . . . . . . . . . . . . . . . . . . . 43
4.1 Description of the Newly Proposed BBW System . . . . . . . . . . . . . . . . . . . . . . 45
4.2 Control Algorithm Design for Hydraulic Pump-Based Pressure
Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.3 Control Algorithm Design for Closed-Loop
Pressure-Difference-Limiting Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.1 Linear Modulation of On/Off Valve . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.2 Closed-Loop Pressure-Difference-Limiting Control . . . . . . . . . . . . . 53
4.4 Hardware-in-the-Loop Test Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.4.1 Comparison of HPBPM and CLPDL Control . . . . . . . . . . . . . . . . . 56
4.4.2 Brake Blending Algorithm Based on CLPDL Modulation . . . . . . . . 59
5
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Authors’ Biographies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
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