Modelling, Estimation and AI Applications for Lithium-Ion Battery Management Systems
Language: English
Published by Elsevier Science Sep 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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US$ 292.83
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Add to basketItem description from seller
Neuware - Modelling, Estimation and AI Applications for Lithium-Ion Battery Management Systems is a comprehensive guide to the latest advancements in integrating artificial intelligence with lithium-ion battery technology. The book offers an in-depth exploration of fundamental principles, advanced modeling techniques, and state estimation strategies that are vital for enhancing battery performance, safety, and longevity. The book presents systematic coverage of battery operation, performance testing methods, and application scenarios, providing a solid foundation for understanding current challenges and innovations. Sections delve into core AI algorithms, including machine learning, deep learning, and hybrid approaches, illustrating how they revolutionize battery modeling and health monitoring.Key topics include hybrid modeling methods that combine equivalent circuit models, electrochemical theories, and AI techniques; precise estimation of State of Charge (SOC), State of Health (SOH), and State of Power (SOP); and strategies for joint state estimation to facilitate comprehensive battery management. Practical insights are reinforced with detailed discussions on experimental platform design, validation procedures, and data visualization techniques, bridging theory and real-world engineering. …
Seller Inventory # 9780443453472
- Title
- Modelling, Estimation and AI Applications for Lithium-Ion Battery Management Systems
- Author
- Qi Huang
- Publisher
- Elsevier Science Sep 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0443453470
- ISBN 13
- 9780443453472
- Item weight
- 449 grams
- Dimensions
- 229x152x34 mm
Key topics include hybrid modeling methods that combine equivalent circuit models, electrochemical theories, and AI techniques; precise estimation of State of Charge (SOC), State of Health (SOH), and State of Power (SOP); and strategies for joint state estimation to facilitate comprehensive battery management. Practical insights are reinforced with detailed discussions on experimental platform design, validation procedures, and data visualization techniques, bridging theory and real-world engineering.
- Utilizes machine learning and deep learning as the core driving force, integrating traditional equivalent circuit models with data-driven models to overcome the limitations of single physical modeling or scenario constraints
- Introduces the SOP (State-Of-Power) Estimation and multi-state joint optimization framework to address coupling defects inherent in traditional independent estimation methods
- Teaches how to build a comprehensive chain encompassing “AI algorithm foundation ― multi-model fusion ― full lifecycle management ― experimental verification” to ensure robustness and reliability of Li-ion batteries
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About the Author
Huang Qi is a professor and doctoral supervisor, IEEE Fellow, IETF fellow, expert enjoying special government allowance of the State Council, candidate of New Century Excellent Talents Support Program of Ministry of Education, academic and technical leader of Sichuan Province, head of Youth Science and Technology Innovation Team of Sichuan Province, candidate of "Tianfu Qingcheng Innovation Leading Talents Program" of Sichuan Province. His main research fields are: new power energy systems, science and technology innovation strategy, and development planning etc. He has undertaken more than 20 national and provincial projects such as National Key R & D Plan and National Natural Science Foundation, and won 1 China Patent Excellence Award, 1 First Prize of China Instrument Society, 1 First Prize/Second Prize of Sichuan Province Science and Technology Progress Award, and 1 Second Prize of Excellent Scientific and Technological Achievements of Ministry of Education. He has published more than 300 academic papers, including more than 200 SCI papers, published 5 monographs, including 2 Wiley-IEEE monographs, applied for more than 100 patents, and obtained more than 80 authorized national invention patents and 2 US patents. He served as a member of the Expert Group on Science and Technology Innovation Planning in the Energy Field of the 13th Five-Year Plan of the Ministry of Science and Technology, the leader of the Expert Group on the 14th Five-Year Plan in the Energy and Chemical Industry Field of Sichuan Province, the convener of the 13th Five-Year Plan in the New Energy Field of Sichuan Province, and the leader of the Expert Group on "Clean Energy" in the "5+1" Modern Industrial System of Sichuan Province. He served as the chairman of several high-level international conferences, such as the 2019 IEEPES Innovative Smart Grid Technology and the 2022 IEEEI 2.
Liya Zhang is a Doctoral Student at Inner Mongolia University of Technology. As a key member, she participated in 5 major research projects, including the National Key Research and Development Program and the National Natural Science Foundation. Authorized 2 domestic invention patents and won the Gold Award at the BRICS Invention Exhibition.
Guangchen Liu, Professor at Inner Mongolia University of Technology. Mainly engaged in teaching and research work in the fields of new energy generation and energy storage, power electronics, and power transmission. Hosted multiple projects such as the National Natural Science Foundation, Inner Mongolia Science and Technology Major Special Project, and Inner Mongolia Science and Technology Plan, published over 70 academic papers, and authored 3 monographs/textbooks. The research achievements are mainly used to solve the problem of high-quality power supply in remote areas.
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.
"About the title" may belong to another edition of this title.
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