Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs (eBook)

This book investigates in detail long-term health state estimation technology of energy storage systems, assessing its potential use to replace common filtering methods that constructs by equivalent circuit model with a data-driven method combined with electrochemical modeling, which can reflect the battery internal characteristics, the battery degradation modes, and the battery pack health state. Studies on long-term health state estimation have attracted engineers and scientists from various disciplines, such as electrical engineering, materials, automation, energy, and chemical engineering. Pursuing a holistic approach, the book establishes a fundamental framework for this topic, while emphasizing the importance of extraction for health indicators and the significant influence of electrochemical modeling and data-driven issues in the design and optimization of health state estimation in energy storage systems. The book is intended for undergraduate and graduate students who are interested in new energy measurement and control technology, researchers investigating energy storage systems, and structure/circuit design engineers working on energy storage cell and pack.

Qi Huang is the president of Southwest University of Science and Technology, China. He is an IEEE fellow (Conference Secretary General). He is an authoritative expert in the field of power systems and energy internet research. He is the head of NELab. He has published 2 Wiley-IEE monographs and more than 300 academic papers. He has applied for more than 100 patents, and he has been granted more than 60 national invention patents and 1 American patent.

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 deputy head of NELab, modeling, and state estimation strategy research for lithium-ion batteries. He has undertaken more than 40 projects and 30 patents, published more than 150 research papers as well as won 20 awards such as the Young Scholar and Science & Technology Progress Awards.

Zonghai Chen is a professor at the University of Science and Technology of China, China. His research interests include energy saving and new energy vehicle technology, complex system modeling, simulation and control, fuel cell system management, and optimal control. He has published more than 400 academic papers and applied for more than 40 patents.

Ran Xiong is a postgraduate student at Southwest University of Science and Technology, China. He is one of the group leaders of NELab. He is responsible for the electrochemical modeling and the health state estimation of energy storage batteries in NELab. He has participated in 5 projects and 6 patents, assisted in writing 3 academic monographs, and published 4 research papers as the first author or corresponding author, including 3 SCI papers.

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

Daniel-I. Stroe is an associate professor with AAU Energy, Aalborg University, Denmark, and the leader of the Batteries research group. He received his Ph.D. degree in lifetime modeling of lithium-ion batteries from Aalborg University in 2010. He has co-authored one book and over 150 scientific peer-review publications on battery performance, modeling, and state estimation. His research interests include energy storage systems for grid and e-mobility, lithium-based battery testing, modeling, lifetime estimation, and diagnostics.

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This book investigates in detail long-term health state estimation technology of energy storage systems, assessing its potential use to replace common filtering methods that constructs by equivalent circuit model with a data-driven method combined with electrochemical modeling, which can reflect the battery internal characteristics, the battery degradation modes, and the battery pack health state. Studies on long-term health state estimation have attracted engineers and scientists from various disciplines, such as electrical engineering, materials, automation, energy, and chemical engineering. Pursuing a holistic approach, the book establishes a fundamental framework for this topic, while emphasizing the importance of extraction for health indicators and the significant influence of electrochemical modeling and data-driven issues in the design and optimization of health state estimation in energy storage systems. The book is intended for undergraduate and graduate students who are interested in new energy measurement and control technology, researchers investigating energy storage systems, and structure/circuit design engineers working on energy storage cell and pack.