Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management
- 1st Edition - July 1, 2024
- Authors: Jili Tao, Ridong Zhang, Longhua Ma
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 1 8 9 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 1 9 0 - 5
Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state of the art in hybrid electric vehicle system modeling and management. With a… Read more
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Request a sales quoteApplication of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state of the art in hybrid electric vehicle system modeling and management. With a focus on learning-based energy management strategies, this book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.
This book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence–based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multiobjective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, this book also introduces State of Charge and State of Health prediction methods and real-time driving pattern recognition. For each application, the detailed experimental process, program code, experimental results, and algorithm performance evaluation are provided.
Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management is a valuable reference for anyone involved in the modeling and management of hybrid electric vehicles, and will be of interest to graduate students, researchers, and professionals working on HEVs in the fields of energy, electrical, and automotive engineering.
This book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence–based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multiobjective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, this book also introduces State of Charge and State of Health prediction methods and real-time driving pattern recognition. For each application, the detailed experimental process, program code, experimental results, and algorithm performance evaluation are provided.
Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management is a valuable reference for anyone involved in the modeling and management of hybrid electric vehicles, and will be of interest to graduate students, researchers, and professionals working on HEVs in the fields of energy, electrical, and automotive engineering.
- Provides a guide to the modeling and simulation methods of hybrid electric vehicle energy systems, including fuel cell systems
- Describes the fundamental concepts and theory behind CNN, MPC, fuzzy control, multi objective optimization, fuzzy Q-learning and DDPG
- Explains how to use energy management methods such as parameter estimation, Q-learning, and pattern recognition, including battery State of Health and State of Charge prediction, and vehicle operating conditions
Students and researchers in energy, electrical, and automotive engineering or interested in energy management or EVs, Professional energy, electrical, and automotive engineers involved in hybrid vehicle control, optimization, and vehicle manufacturing, used as a special textbook for undergraduate and graduate students majoring in vehicle engineering.
Preface
Acknowledgments
1. Introduction
2. System modeling of lithiumeion battery, PEMFC, and supercapacitor in HEV
3. Neural network modeling for SOH of lithium-ion battery and performance degradation prediction of fuel cell
4.Optimal fuzzy energy management for fuel cell/supercapacitor systems using neural network-based driving pattern recognition
5. Optimal fuzzy energy management system optimization based on NSGA-III-SD for lithium battery/supercapacitor HEV
6. Q learning-based hybrid energy management strategy
7. Improved DDPG hybrid energy management strategy based on LSH
8. Further idea on meta EMS for HEV
Index
Acknowledgments
1. Introduction
2. System modeling of lithiumeion battery, PEMFC, and supercapacitor in HEV
3. Neural network modeling for SOH of lithium-ion battery and performance degradation prediction of fuel cell
4.Optimal fuzzy energy management for fuel cell/supercapacitor systems using neural network-based driving pattern recognition
5. Optimal fuzzy energy management system optimization based on NSGA-III-SD for lithium battery/supercapacitor HEV
6. Q learning-based hybrid energy management strategy
7. Improved DDPG hybrid energy management strategy based on LSH
8. Further idea on meta EMS for HEV
Index
- No. of pages: 350
- Language: English
- Edition: 1
- Published: July 1, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780443131899
JT
Jili Tao
Jili Tao received the B.Sc. and M.Sc. degrees from Central South University, Changsha, China, in 2001 and 2004, respectively, and the Ph.D. degree from Zhejiang University, Hangzhou, China, in 2007. She is currently an Associate Professor with the Institute of Ningbo Technology, Zhejiang University, Ningbo, China. Her current research interests include intelligent optimization, modeling, and its applications to electronic system design and control system design for HEV, chemical processes.
Affiliations and expertise
Institute of Ningbo Technology, Zhejiang University, ChinaRZ
Ridong Zhang
Ridong Zhang received the Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2007. From 2012 to 2016, he was a Visiting Professor with the Chemical and Biomolecular Engineering Department, The Hong Kong University of Science and Technology, Hong Kong. He is currently a Professor with the Institute of Information and Control, Hangzhou Dianzi University, Hangzhou. His current research interests include modeling and control for chemical nonlinear systems and HEV.
Affiliations and expertise
Institute of Information and Control, Hangzhou Dianzi University, ChinaLM
Longhua Ma
Longhua Ma received the B.S. degree in industrial electrical automation from Lanzhou Jiaotong University,Lanzhou, China, in 1986, the M.S. degree and Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 1993 and 2002, respectively. He was an associate research fellow with National engineering research center for industrial automation, Zhejiang University, Hangzhou, China from 1993 to 2008.From 2008 to 2012, he was an associate professor with School of aeronautics and astronautics, Zhejiang University, Hangzhou, China. Currently, he is a professor with Ningbo Industrial Internet Institute, Ningbo, China. He has (co)author four books and published over 70 international journal and conference papers. His currently research interests include network security, new energy and electric vehicle energy management and control and inertial navigation theory and application.
Affiliations and expertise
Zhejiang University, Hangzhou, China