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Intelligent Control of Connected Plug-in Hybrid Electric Vehicles 2019 ed. [Kõva köide]

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  • Formaat: Hardback, 198 pages, kõrgus x laius: 235x155 mm, kaal: 561 g, 116 Illustrations, color; 12 Illustrations, black and white; XIV, 198 p. 128 illus., 116 illus. in color., 1 Hardback
  • Sari: Advances in Industrial Control
  • Ilmumisaeg: 09-Oct-2018
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030003132
  • ISBN-13: 9783030003135
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Intelligent Control of Connected Plug-in Hybrid Electric Vehicles 2019 ed.Suurem pilt
  • Formaat: Hardback, 198 pages, kõrgus x laius: 235x155 mm, kaal: 561 g, 116 Illustrations, color; 12 Illustrations, black and white; XIV, 198 p. 128 illus., 116 illus. in color., 1 Hardback
  • Sari: Advances in Industrial Control
  • Ilmumisaeg: 09-Oct-2018
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030003132
  • ISBN-13: 9783030003135
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030003135.html
Intelligent Control of Connected Plug-in Hybrid Electric Vehicles presents the development of real-time intelligent control systems for plug-in hybrid electric vehicles, which involves control-oriented modelling, controller design, and performance evaluation. The controllers outlined in the book take advantage of advances in vehicle communications technologies, such as global positioning systems, intelligent transportation systems, geographic information systems, and other on-board sensors, in order to provide look-ahead trip data. The book contains simple and efficient models and fast optimization algorithms for the devised controllers to address the challenge of real-time implementation in the design of complex control systems.

Using the look-ahead trip information, the authors of the book propose intelligent optimal model-based control systems to minimize the total energy cost, for both grid-derived electricity and fuel. The multilayer intelligent control system proposed consists of trip planning, an ecological cruise controller, and a route-based energy management system. An algorithm that is designed to take advantage of previewed trip information to optimize battery depletion profiles is presented in the book. Different control strategies are compared and ways in which connecting vehicles via vehicle-to-vehicle communication can improve system performance are detailed.

Intelligent Control of Connected Plug-in Hybrid Electric Vehicles is a useful source of information for postgraduate students and researchers in academic institutions participating in automotive research activities. Engineers and designers working in research and development for automotive companies will also find this book of interest.

Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

1 Introduction 1(4)
1.1 Background
1(1)
1.2 Motivation and Challenges
2(1)
1.3 Objectives and Methods
3(1)
1.4 Book Organization
4(1)
References
4(1)
2 Related Work 5(16)
2.1 Trip Planning
5(1)
2.2 HEV/PHEV Energy Management Strategies
6(6)
2.2.1 Dynamic Programming
7(1)
2.2.2 Pontryagin's Minimum Principle
8(1)
2.2.3 Model Predictive Control
8(1)
2.2.4 Explicit Model Predictive Control
9(2)
2.2.5 Control-Relevant Parameter Estimated eMPC
11(1)
2.2.6 Equivalent Consumption Minimization Strategy
12(1)
2.3 Cruise Controller
12(2)
2.3.1 Adaptive Cruise Controller
13(1)
2.3.2 Ecological Cruise Controller
13(1)
2.4 Summary
14(1)
References
15(6)
3 High-Fidelity Modeling of a Plug-in Hybrid Electric Powertrain 21(24)
3.1 Introduction
21(1)
3.2 Toyota Prius Plug-in Hybrid Powertrain
22(2)
3.3 High-Fidelity Model in MapleSim
24(3)
3.3.1 Mean-Value Internal Combustion Engine
24(2)
3.3.2 Electric Machines
26(1)
3.3.3 Lithium-Ion Battery Pack
26(1)
3.3.4 Power-Split Device
26(1)
3.3.5 Vehicle Model
27(1)
3.4 Model Validation
27(7)
3.4.1 Mean-Value Internal Combustion Engine
28(1)
3.4.2 Electric Machines
29(1)
3.4.3 Lithium-Ion Battery Pack
30(3)
3.4.4 Power-Split Device
33(1)
3.4.5 Vehicle Model
33(1)
3.5 High-Fidelity Model in Autonomie
34(6)
3.5.1 Powertrain Model
35(3)
3.5.2 Driver Model
38(1)
3.5.3 Powertrain Controller
39(1)
3.6 Summary
40(1)
References
40(5)
Part I: Energy Management Approach
4 Nonlinear Model Predictive Control
45(34)
4.1 NMPC Energy Management Design
46(18)
4.1.1 Theory of Model Predictive Control (MPC)
46(4)
4.1.2 NMPC Performance on the Low-Fidelity Powertrain Model
50(9)
4.1.3 NMPC Performance Benchmarking
59(1)
4.1.4 NMPC Performance on the High-Fidelity Powertrain Model
60(4)
4.2 Low-Level Controls Design
64(10)
4.2.1 Engine Control-Oriented Model
65(2)
4.2.2 Engine Controls Design
67(1)
4.2.3 Results of Simulation
68(3)
4.2.4 With Emissions Control
71(3)
4.3 Summary
74(1)
References
75(4)
5 Multi-parametric Predictive Control
79(24)
5.1 eMPC Energy Management Strategy Design
80(6)
5.1.1 Control-Oriented Model
81(1)
5.1.2 Optimization Problem Formulation
82(2)
5.1.3 Region Reduction
84(1)
5.1.4 Point Location Problem
85(1)
5.2 Energy Management Polytopes
86(3)
5.3 Stability Notes
89(4)
5.4 eMPC Performance Simulation
93(5)
5.4.1 No Knowledge of Trip Information
94(1)
5.4.2 Known Travelling Distance
94(2)
5.4.3 Discussions
96(2)
5.5 eMPC Performance Benchmarking via HIL
98(3)
5.6 Summary
101(1)
References
101(2)
6 Control-Relevant Parameter Estimated Strategy
103(24)
6.1 Control-Relevant Parameter Estimation (CRPE)
103(5)
6.1.1 Battery Thevenin Model
104(1)
6.1.2 Battery Parameters Estimation
105(3)
6.1.3 CRPE Control-Oriented Model
108(1)
6.2 CRPE-eMPC Energy Management Polytopes
108(4)
6.2.1 CRPE-eMPC Controls Regions
109(1)
6.2.2 CRPE-eMPC Stability Notes
110(2)
6.3 CRPE-eMPC Performance Simulation
112(6)
6.3.1 No Knowledge of Trip Information
112(1)
6.3.2 Known Traveling Distance
113(1)
6.3.3 Discussions
113(5)
6.4 CRPE-eMPC Performance Benchmarking via HIL
118(4)
6.5 Summary
122(1)
References
123(4)
Part II: Smart Ecological Supervisory Controls
7 Real-Time Trip Planning Module Development and Evaluation
127(18)
7.1 Online Optimization Model
128(2)
7.2 Real-Time Optimization Procedure
130(3)
7.2.1 Dynamic Programming
131(1)
7.2.2 Real-Time Cluster-Based Optimization
132(1)
7.3 Benchmarking via MIL and HIL
133(10)
7.3.1 MIL Testing
133(2)
7.3.2 HIL Testing
135(8)
7.4 Summary
143(1)
References
143(2)
8 Route-Based Supervisory Controls
145(24)
8.1 Optimum Energy Management Development
145(6)
8.1.1 Pontryagin's Minimum Principle
147(2)
8.1.2 Route-Based EMS
149(2)
8.1.3 Level of Trip Information
151(1)
8.2 MIL Testing
151(12)
8.2.1 Following Standard Driving Cycles
151(8)
8.2.2 Comparison with MPC Controller
159(4)
8.3 Control Prototyping via HIL
163(4)
8.3.1 Controller Prototyping
164(1)
8.3.2 HIL Testing Results
164(3)
8.4 Summary
167(1)
References
167(2)
9 Ecological Cruise Control
169(16)
9.1 Control-Oriented Modeling
169(2)
9.2 Controls Design
171(4)
9.2.1 Nonlinear Model Predictive Control
172(2)
9.2.2 Linear Model Predictive Control
174(1)
9.3 Results
175(4)
9.4 HIL Testing Results
179(3)
9.4.1 Controller Prototyping
179(2)
9.4.2 HIL Testing Results
181(1)
9.5 Summary
182(1)
References
182(3)
10 Conclusions
185(6)
10.1 Part I
185(1)
10.2 Part II
186(1)
10.3 Recommendations for Future Research
187(4)
10.3.1 Controls Design
188(1)
10.3.2 Controls Validation
189(1)
10.3.3 Smart PHEV
189(2)
Appendix A: Hardware-in-the-Loop Procedure 191
Amir Taghavipour received his BASc. and MASc. degrees in Mechanical Engineering from Sharif University ofTechnology, Tehran, Iran in 2007 and 2010, respectively. He obtained his PhD at the Systems Design Engineering Department, University of Waterloo in 2014 on design and implementation of a real-time optimal energy management systems for plug-in hybrid electric vehicles in collaboration with Toyota Technical Centre North America and MapleSoft Canada. He is currently an Assistant Professor with the Department of Mechanical Engineering, K. N. Toosi University of Technology. His research mainly focuses on model-based and real-time controllers design for Mechatronic and sustainable energy systems, especially energy management systems for full electric, hybrid (electric and hydraulic), plug-in hybrid and fuel cell-powered vehicles. His research interests include intelligent hybrid and electric vehicles, automotive systems control, modeling and prototyping, model predictive control, nonlinear and hybrid systems, and optimization approaches. Mahyar Vajedi received the B.Sc. and M.Sc. degrees in mechanical engineering from Sharif University of Technology; and the PhD degree in Systems Design Engineering from University of Waterloo. He is currently a Postdoctoral Fellow at the Smart Hybrid and Electric Vehicle Systems Laboratory, University of Waterloo. He is the author of several published papers. His research interests include intelligent vehicle control systems, such as eco-cruise controllers, adaptive cruise controllers, and intelligent energy management systems for electric propulsion vehicles. Nasser L. Azad is currently an Associate Professor with the Department of Systems Design Engineering, University of Waterloo. His research program operates at the intersection of advanced vehicle electrification and vehicle communication technologies, where he specializes in the development ofintelligent automotive controllers that can leverage information provided by emerging ITS, GPS, GIS, and advanced wireless technologies to optimize key performance characteristics, such as fuel economy and emission reduction, in real time. The multidisciplinary nature of his research has allowed him to establish strong collaborative networks with researchers from universities in Europe, Asia, and the U.S., as well as with world-leading car manufacturers. He received an Early Researcher Award in 2015.