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E-raamat: Optimization and Optimal Control in Automotive Systems

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Optimization and Optimal Control in Automotive SystemsSuurem pilt
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This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization.

Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applied to system, sub-system or component design parameters and is performed based on system models; others require applications of optimization directly to experimental systems to determine either optimal calibration or the optimal control trajectory/control law.

Optimization and Optimal Control in Automotive Systems reflects the state-of-the-art in and promotes a comprehensive approach to optimization in automotive systems by addressing its different facets, by discussing basic methods and showing practical approaches and specific applications of optimization to design and control problems for automotive systems. The book will be of interest both to academic researchers, either studying optimization or who have links with the automotive industry and to industrially-based engineers and automotive designers.

Part I Optimization Methods
1 Trajectory Optimization: A Survey
3(20)
Anil V. Rao
1.1 Introduction
3(1)
1.2 Trajectory Optimization Problem
4(1)
1.3 Numerical Methods for Trajectory Optimization
5(1)
1.4 Numerical Solution of Differential Equations
6(3)
1.4.1 Collocation
7(1)
1.4.2 Integration of Functions
8(1)
1.5 Nonlinear Optimization
9(1)
1.6 Methods for Solving Trajectory Optimization Problems
9(6)
1.6.1 Indirect Methods
10(3)
1.6.2 Direct Methods
13(2)
1.7 Software for Solving Trajectory Optimization Problems
15(1)
1.8 Choosing a Method
16(1)
1.9 Applications to Automotive Systems
17(1)
1.10 Conclusions
17(6)
References
18(5)
2 Extremum Seeking Methods for Online Automotive Calibration
23(18)
Chris Manzie
Will Moase
Rohan Shekhar
Alireza Mohammadi
Dragan Nesic
Ying Tan
2.1 Introduction
23(3)
2.2 Review of Extremum Seeking
26(5)
2.2.1 Black-Box Extremum Seeking
27(2)
2.2.2 Grey-Box Extremum Seeking
29(1)
2.2.3 Sampled Data Approaches
30(1)
2.3 Application to Automotive Engine Calibration
31(5)
2.4 Incorporation of Constraints
36(1)
2.5 Summary and Future Opportunities
37(4)
References
37(4)
3 Model Predictive Control of Autonomous Vehicles
41(18)
Mario Zanon
Janick V. Frasch
Milan Vukov
Sebastian Sager
Moritz Diehl
3.1 Introduction
41(1)
3.2 Control and Estimation Problems
42(2)
3.2.1 Nonlinear Model Predictive Control
42(1)
3.2.2 Moving Horizon Estimation
43(1)
3.3 Efficient Algorithms for fast NMPC and MHE
44(2)
3.3.1 Online Solution of the Dynamic Optimization Problem
44(1)
3.3.2 Fast Solvers Based on Automatic Code Generation
45(1)
3.4 Vehicle Model
46(4)
3.4.1 Chassis Dynamics
46(1)
3.4.2 Tire Contact Forces: Pacejka's Magic Formula
47(1)
3.4.3 Wheel Dynamics
48(1)
3.4.4 Vertical Forces and Suspension Model
48(1)
3.4.5 Spatial Reformulation of the Dynamics
49(1)
3.5 Control of Autonomous Vehicles
50(5)
3.5.1 MHE Formulation
50(1)
3.5.2 MPC Formulation
51(2)
3.5.3 Simulation Results
53(1)
3.5.4 Treating Gear Shifts
54(1)
3.6 Conclusions and Outlook
55(4)
References
55(4)
4 Approximate Solution of HJBE and Optimal Control in Internal Combustion Engines
59(18)
Mario Sassano
Alessandro Astolfi
4.1 Introduction
59(1)
4.2 Hamilton-Jacobi-Bellman Equation and Optimal Control
60(1)
4.3 Dynamic Value Function and Algebraic P Solution
61(7)
4.3.1 Definition of Dynamic Value Function
62(2)
4.3.2 A Class of Canonical Dynamic Value Functions
64(2)
4.3.3 Minimization of the Extended Cost
66(2)
4.4 Optimal Control in Internal Combustion Engine Test Benches
68(4)
4.5 Conclusions
72(5)
References
73(4)
Part II Inter and Intra Vehicle System Optimization
5 Intelligent Speed Advising Based on Cooperative Traffic Scenario Determination
77(16)
Rodrigo H. Ordonez-Hurtado
Wynita M. Griggs
Kay Massow
Robert N. Shorten
5.1 Introduction
77(1)
5.2 Intelligent Speed Adaptation System
78(1)
5.3 Procedure
79(1)
5.4 Methodology: First Stage
80(4)
5.4.1 Selection of the Next Point of Interest and the Next Vehicle
80(1)
5.4.2 Vehicular Density Estimation
81(1)
5.4.3 Traffic Scenario Determination
81(3)
5.5 Methodology: Second Stage
84(3)
5.5.1 Updating Speed in Virtual Next Vehicles
84(1)
5.5.2 Proposed Recommended Speed Scheme
85(1)
5.5.3 Proposed Recommended Distance Scheme
86(1)
5.6 Validation
87(4)
5.6.1 Traffic Scenario Determination
88(2)
5.6.2 Recommended Speed
90(1)
5.6.3 Recommended Distance
91(1)
5.7 Conclusions and Future Work
91(2)
References
92(1)
6 Driver Control and Trajectory Optimization Applied to Lane Change Maneuver
93(16)
Patrick J. McNally
6.1 Background
93(2)
6.1.1 Experiential Engineering
94(1)
6.1.2 Lane Change Problem
94(1)
6.2 Model Based Engineering Environment for Objective Evaluation
95(5)
6.2.1 Determination of Driver Controls
95(2)
6.2.2 Optimization Problem
97(2)
6.2.3 Offline Optimization Results
99(1)
6.3 Virtual Prototyping Environment for Subjective Evaluation
100(4)
6.3.1 Driver Maneuvers in a Controlled Experiment
102(2)
6.4 Driving Simulator Results (Online)
104(1)
6.4.1 Imposing Constraints on Simulated Driver Controls
104(1)
6.5 Conclusions
105(4)
References
107(2)
7 Real-Time Near-Optimal Feedback Control of Aggressive Vehicle Maneuvers
109(22)
Panagiotis Tsiotras
Ricardo Sanz Diaz
7.1 Introduction
109(3)
7.2 Aggressive Yaw Maneuver of a Speeding Vehicle
112(6)
7.2.1 Problem Statement
112(1)
7.2.2 Vehicle and Tire Model
113(3)
7.2.3 Optimal Control Formulation
116(2)
7.3 Statistical Interpolation Using Gaussian Processes
118(4)
7.3.1 Basic Theory
118(3)
7.3.2 Choice of Correlation Functions
121(1)
7.4 Application to On-line Aggressive Vehicle Maneuver Generation
122(3)
7.4.1 Feedback Controller Synthesis
122(3)
7.4.2 Numerical Results
125(1)
7.5 Conclusions
125(6)
References
127(4)
8 Applications of Computational Optimal Control to Vehicle Dynamics
131(16)
Josko Deur
Mirko Coric
Josip Kasac
Francis Assadian
Davor Hrovat
8.1 Introduction
131(1)
8.2 Overview of Previous Optimization and Assessment Results
132(7)
8.2.1 Optimization Algorithm
132(1)
8.2.2 Vehicle Model
133(2)
8.2.3 Formulation of Optimization Problem
135(1)
8.2.4 Optimization and Assessment Results
136(3)
8.3 Detailed Optimization for Active Steering Configurations
139(5)
8.3.1 Optimization Algorithm
139(1)
8.3.2 Active Rear Steering (ARS)
139(3)
8.3.3 Active Front Steering (AFS)
142(1)
8.3.4 Four Wheel Steering (4WS = ARS & AFS)
143(1)
8.4 Conclusion
144(3)
References
145(2)
9 Stochastic Fuel Efficient Optimal Control of Vehicle Speed
147(16)
Kevin McDonough
Ilya Kolmanovsky
Dimitar Filev
Steve Szwabowski
Diana Yanakiev
John Michelini
9.1 Introduction
147(1)
9.2 Modeling for SDP Policy Generation
148(6)
9.2.1 Longitudinal Vehicle Dynamics
149(1)
9.2.2 Stochastic Models of Reference Speed and Road Grade
150(1)
9.2.3 Cost Function Constituents
151(3)
9.3 Stochastic Dynamic Programming
154(1)
9.4 Simulation Case Studies
154(5)
9.4.1 In-traffic Driving
155(2)
9.4.2 Optimal Vehicle Following
157(2)
9.5 Vehicle Experiments
159(1)
9.6 Concluding Remarks
160(3)
References
161(2)
10 Predictive Cooperative Adaptive Cruise Control: Fuel Consumption Benefits and Implementability
163(18)
Dominik Lang
Thomas Stanger
Roman Schmied
Luigi del Re
10.1 Introduction
164(1)
10.2 Problem Statement
164(3)
10.2.1 Casting the Problem into the Mathematical Form
165(2)
10.3 Assessment of Potential
167(1)
10.4 Nonlinear Receding Horizon Optimization
168(1)
10.5 Approximate Control Law Within the Linear MPC Framework
169(2)
10.6 Approximate Control Law Utilizing an Identified Hammerstein--Wiener Model
171(3)
10.7 Traffic Prediction Model From Data
174(3)
10.8 Conclusions and Outlook
177(4)
References
177(4)
Part III Powertrain Optimization
11 Topology Optimization of Hybrid Power Trains
181(18)
Theo Hofman
Maarten Steinbuch
11.1 Introduction
181(5)
11.1.1 Co-design Methods
182(1)
11.1.2 Problem Definition: System Design Optimization
183(1)
11.1.3 Outline and Contribution of the
Chapter
184(2)
11.2 Control Design Optimization: Gear Shift Strategies with Comfort Constraints for Hybrid Vehicles
186(7)
11.2.1 Bi-level Optimization: Control Problem
188(4)
11.2.2 Simulation Result: Bi-level Propulsion System and Control Design
192(1)
11.3 Control and Propulsion System Design Optimization: Topology, Transmission, Size and Control Optimization for Hybrid Vehicles
193(3)
11.3.1 Simulation Result: Bi-level Propulsion System and Control Design
195(1)
11.4 Conclusions
196(3)
References
197(2)
12 Model-Based Optimal Energy Management Strategies for Hybrid Electric Vehicles
199(20)
Simona Onori
12.1 Introduction
199(1)
12.2 Optimization Problems in HEVs
200(1)
12.3 Case Study: Pre-transmission Parallel Hybrid
201(1)
12.4 Problem Formulation
202(3)
12.4.1 Optimal Energy Management Problem in HEVs
203(2)
12.5 Finite-Time Horizon Energy Management Strategies
205(2)
12.6 Motivation for Infinite-Time Horizon Optimization
207(1)
12.7 From Finite-Time to Infinite-Time Horizon Optimal Control Problem
208(2)
12.7.1 System Dynamics Reformulation
209(1)
12.8 Infinite-Time Nonlinear Optimal Control Strategy (NL-OCS)
210(4)
12.9 Strategies Comparison: Simulation Results
214(2)
12.10 Conclusions
216(3)
References
217(2)
13 Optimal Energy Management of Automotive Battery Systems Including Thermal Dynamics and Aging
219(18)
Antonio Sciarretta
Domenico di Domenico
Philippe Pognant-Gros
Gianluca Zito
13.1 Introduction
219(2)
13.2 Case Study and Motivation
221(1)
13.3 Optimal Control Problem Formulation
222(6)
13.3.1 Powertrain Modeling
223(2)
13.3.2 Battery Modeling
225(1)
13.3.3 Battery Aging Modeling
226(2)
13.4 Optimal Control Problem Solution
228(2)
13.4.1 Dynamic Programming
228(1)
13.4.2 PMP
229(1)
13.5 Optimal Control Problem Results
230(5)
13.5.1 Dynamic Programming Results
230(4)
13.5.2 PMP Results
234(1)
13.6 Conclusions
235(2)
References
236(1)
14 Optimal Control of Diesel Engines with Waste Heat Recovery System
237(20)
Frank Willems
M. C. F. Donkers
Frank Kupper
14.1 Introduction
238(1)
14.2 System Description
238(4)
14.2.1 Simulation Model
239(2)
14.2.2 Control Model
241(1)
14.3 Control Strategy
242(4)
14.3.1 An Optimal Control Approach to IPC
243(1)
14.3.2 Optimal IPC Trategy
243(1)
14.3.3 Real-Time IPC Strategy
244(1)
14.3.4 Baseline Strategy
245(1)
14.4 Control Design
246(1)
14.4.1 Optimal IPC Strategy
246(1)
14.4.2 Real-Time IPC Strategy
247(1)
14.5 Simulation Results
247(5)
14.5.1 Overall Powertrain Results
248(1)
14.5.2 Cold Cycle Results
249(3)
14.6 Conclusions and Future Work
252(5)
References
253(4)
Part IV Optimization of the Engine Operation
15 Learning Based Approaches to Engine Mapping and Calibration Optimization
257(16)
Dimitar Filev
Yan Wang
Ilya Kolmanovsky
15.1 Introduction
257(2)
15.2 Mathematical Problem Formulation
259(1)
15.3 Jacobian Learning Based Optimization Algorithm
260(3)
15.4 Case Study 1: Application to Engine Mapping
263(2)
15.5 Case Study 2: On-board Fuel Consumption Optimization in Series HEV
265(1)
15.6 Predictor-Corrector Algorithm
266(3)
15.7 Case Study 2 (Cont'd): On-board Fuel Consumption Optimization in Series HEV
269(1)
15.8 Concluding Remarks
269(4)
References
271(2)
16 Online Design of Experiments in the Relevant Output Range
273(18)
Nico Didcock
Andreas Rainer
Stefan Jakubek
16.1 Introduction
274(1)
16.2 State of the Art Development Approach
274(3)
16.3 Mathematical Background of the COR Design
277(4)
16.3.1 A Local Model Architecture
279(1)
16.3.2 State of the Art Designs
280(1)
16.3.3 Online Procedures
280(1)
16.4 Design Strategies
281(2)
16.4.1 A Distance Criterion in the Product Space
281(1)
16.4.2 The Custom Output Region (COR)
282(1)
16.4.3 The iDoE Strategy
282(1)
16.5 Improved Development Approach using the COR Design
283(2)
16.6 Further Improvement
285(3)
16.7 Conclusion
288(3)
References
289(2)
17 Optimal Control of HCCI
291(10)
Per Tunestal
17.1 Introduction
291(1)
17.2 Optimal Control of HCCI
292(6)
17.2.1 Multi-output MPC of HCCI
292(1)
17.2.2 Physics-Based MPC of HCCI Combustion Timing
293(3)
17.2.3 Hybrid MPC of Exhaust Recompression HCCI
296(1)
17.2.4 Optimizing Gains and Fuel Consumption of HCCI Using Extremum Seeking
297(1)
17.3 Conclusions
298(3)
References
300(1)
18 Optimal Lifting and Path Profiles for a Wheel Loader Considering Engine and Turbo Limitations
301(24)
Vaheed Nezhadali
Lars Eriksson
18.1 Introduction
301(2)
18.1.1 Outline
303(1)
18.2 System Model
303(10)
18.2.1 Powertrain and Longitudinal Dynamics
307(3)
18.2.2 Steering and Ground Position
310(1)
18.2.3 Lifting System
311(2)
18.3 Optimal Control Problem Formulation
313(2)
18.4 Results
315(7)
18.4.1 Optimal WL Trajectory from Loading Point to the Load Receiver
315(1)
18.4.2 Min Mf and Min T System Transients
316(3)
18.4.3 Delayed Lifting
319(1)
18.4.4 Power Break Down
319(3)
18.5 Conclusion
322(3)
References
323(2)
Author Index 325
Prof. Luigi del Re is Professor at the Johannes Kepler University Linz, where he is head of the Institute for Design and Control of Mechatronical systems. He has 30 years experience in identification and control of complex systems, in particular of engine and vehicle systems, both in industry and academia. He is the editor of two earlier LNCIS volumes on automotive control: Identification for Automotive Systems (978-1-4471-2220-3) and Automotive Model Predictive Control (978-1-84996-070-0) Ilya Kolmanovsky is a Professor of Aerospace Engineering at the University of Michigan with research interests in control applications to automotive and aerospace systems. Prior to joining the University of Michigan, he had close to 15 years of industrial research experience in powertrain control at Ford Research and Advanced Engineering. Maarten Steinbuch is Distinguished University Professor at Eindhoven University of Technology and head of the ControlSystems Technology group. He has experience both in industry and academia, in the field of control of high-tech systems, in particular high-precision motion systems and automotive power trains. Harald Waschl is a research assistant at the Johannes Kepler University Linz, where he is with the Institute for Design and Control of Mechatronical systems. He has experience in the field of optimal and model-based control of combustion engines and emission modelling.
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