Battery System Modeling - Softcover

Wang, Shunli; Fernandez, Carlos; Chunmei, Yu; Fan, Yongcun; Wen, Cao; Stroe, Daniel-Ioan; Chen, Zonghai

Softcover

ISBN 10: 0323904726 ISBN 13: 9780323904728

Publisher: Elsevier, 2021

Synopsis

Battery System Modeling provides advances on the modeling of lithium-ion batteries. Offering step-by-step explanations, the book systematically guides the reader through the modeling of state of charge estimation, energy prediction, power evaluation, health estimation, and active control strategies. Using applications alongside practical case studies, each chapter shows the reader how to use the modeling tools provided. Moreover, the chemistry and characteristics are described in detail, with algorithms provided in every chapter. Providing a technical reference on the design and application of Li-ion battery management systems, this book is an ideal reference for researchers involved in batteries and energy storage.

Moreover, the step-by-step guidance and comprehensive introduction to the topic makes it accessible to audiences of all levels, from experienced engineers to graduates.

• Explains how to model battery systems, including equivalent, electrical circuit and electrochemical nernst modeling
• Includes comprehensive coverage of battery state estimation methods, including state of charge estimation, energy prediction, power evaluation and health estimation
• Provides a dedicated chapter on active control strategies

About this title

About the Authors

Shunli Wang is a professor at the Southwest University of Science and Technology, China. He is an authoritative expert in the field of new energy research. He is the head of DTlab, modeling, and state estimation strategy research for lithium-ion batteries. He has undertaken more than 40 projects and 30 patents, published more than 100 research papers as well as won 20 awards such as the Young Scholar, and Science & Technology Progress Awards.

Carlos Fernandez is a senior lecturer at Robert Gordon University, Scotland, UK. He received his Ph.D. in Electrocatalytic Reactions from The University of Hull and then worked as a Consultant Technologist in Hull and a post-doctoral position in Manchester. His research interests include Analytical Chemistry, Sensors and Materials, and Renewable Energy.

Yu Chunmei Ph.D was born in Rugao in Jiangsu Province, being interested in state estimation, system identification, and fault diagnosis. Teaching courses as automatic control theory, system identification, and modeling, etc. for undergraduate and postgraduate students. More than 10 research projects have participated in the recent 5 years, such as the Natural science funding, the Provincial Department of Science and Technology, and projects from enterprises. More than 30 papers have been published on various kinds of worldwide academic journals.

Fan Yongcun Ph.D is a core member of the new energy measurement and control research team. Focusing on the measurement and control needs of the new energy field, signal detection and state estimation, anti-interference processing, and control strategy research are carried out to explore the state detection and control theory.

Cao Wen Ph.D main research is based on the battery measurement and control technology, the research of charging algorithms, sensors, and the experimental test device is carried out. In the past five years, he has undertaken more than 10 scientific research projects of the Ministry of education of China and the science and Technology Department of Sichuan Province and published more than 20 research papers together with lab members.

From the Back Cover

Battery System Modeling provides the latest advances on the modeling of lithium-ion batteries. Offering step-by-step explanations, the book systematically guides the reader through the modelling of state of charge estimation, state of energy prediction, state of power evaluation, state of health estimation, and active control strategies. Using applications alongside practical case studies, each chapter also shows the reader how to use the modeling tools provided. Moreover, the chemistry and characteristics are described in detail, and algorithms are provided in every chapter, providing everything needed for accurate and reliable modelling.

Providing a technical reference on the design and application of Li-ion battery management systems, this book is an ideal reference for researchers involved in all aspects of batteries and energy storage devices at various scales. Moreover, the step-by-step guidance and comprehensive introduction to the topic makes it accessible to audiences of all levels, from experienced engineers to graduates.

Edition details

• Publisher: Elsevier
• Publication date: 2021
• Language: English
• ISBN 10: 0323904726
• ISBN 13: 9780323904728
• Binding: Paperback
• Number of pages: 354