Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management - Softcover

Tao

Softcover

ISBN 10: 0443131899 ISBN 13: 9780443131899

Verlag: Elsevier, 2024

Inhaltsangabe

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 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.

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Über die Autorinnen und Autoren

Tao Jili 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.

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.

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.

Von der hinteren Coverseite

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 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.

Key Features:

• 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, fuzzy control, multiobjective optimization, fuzzy Q-learning, and DDPG. The mathematical tools used in these methods are provided alongside MATLAB® and Python codes for every algorithm, as well as case studies on the use of each algorithm
• Explains how to use energy management methods such state estimation, Q-learning, and pattern recognition, including battery State of Health and State of Charge prediction, and vehicle operating conditions

About the Authors
Dr. Jili Tao
Professor, NingboTech University, Ningbo, China

Dr. Ridong Zhang
Professor, Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China

Dr. Longhua Ma
Professor, NingboTech University, Ningbo, China

Details zur Ausgabe

• Verlag: Elsevier
• Erscheinungsdatum: 2024
• Sprache: Englisch
• ISBN 10: 0443131899
• ISBN 13: 9780443131899
• Einband: Taschenbuch
• Anzahl der Seiten: 346
• Kontakt zum Hersteller: ELSEVIER; https://www.elsevier.com/es-es; ; Mooswaldallee 6; Freiburg; 79108; Deutschland