State Estimation Strategies in Lithium-ion Battery Management Systems

Liu, Kailong; Wang PhD, Yujie; Stroe, Daniel-Ioan; Fernandez PhD, Carlos; Guerrero, Josep M.; Wang PhD, Shunli

ISBN 10: 0443161607 / ISBN 13: 9780443161605

Edité par Elsevier, 2023

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N° de réf. du vendeur 18396069881

Détails bibliographiques

• Titre: State Estimation Strategies in Lithium-ion Battery Management Systems
• Éditeur: Elsevier
• Date de parution: 2023
• Langue: anglais
• ISBN à 10 chiffres: 0443161607
• ISBN à 13 chiffres: 9780443161605
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

State Estimation Strategies in Lithium-ion Battery Management Systems presents key technologies and methodologies in modeling and monitoring charge, energy, power and health of lithium-ion batteries. Sections introduce core state parameters of the lithium-ion battery, reviewing existing research and the significance of the prediction of core state parameters of the lithium-ion battery and analyzing the advantages and disadvantages of prediction methods of core state parameters. Characteristic analysis and aging characteristics are then discussed. Subsequent chapters elaborate, in detail, on modeling and parameter identification methods and advanced estimation techniques in different application scenarios.

Offering a systematic approach supported by examples, process diagrams, flowcharts, algorithms, and other visual elements, this book is of interest to researchers, advanced students and scientists in energy storage, control, automation, electrical engineering, power systems, materials science and chemical engineering, as well as to engineers, R&D professionals, and other industry personnel.

• Introduces lithium-ion batteries, characteristics and core state parameters
• Examines battery equivalent modeling and provides advanced methods for battery state estimation
• Analyzes current technology and future opportunities

À propos des auteurs

Kailong Liu is a Professor at the School of Control Science and Engineering, Shandong University, China. His research experience lies at the intersection of AI and electrochemical energy storage applications, especially data science in battery management. His current research is focusing on the development of AI strategies for battery applications.

Yujie Wang is an associate professor at the Department of Automation, University of Science and Technology of China. His research interests include new energy vehicle technology, battery safety management, digital twin and AI application in energy system. He received his Ph.D. degree in Control science and engineering from the University of Science and Technology of China in 2017. He has co-authored over 60 SCI journal papers on battery-related topics. His research interests include energy-saving and new energy vehicle technology, complex system modeling, simulation and control, fuel cell system management, and optimal control.

Daniel-Ioan Stroe Ph.D received the Dipl.-Ing. degree in automatics from the Transylvania University of Brasov, Brasov, Romania, in 2008, and the M.Sc. degree in wind power systems and the Ph.D. degree in lifetime modelling of lithium-ion batteries from Aalborg University, Aalborg, Denmark, in 2010 and 2014, respectively. He is currently an Assistant Professor with the Department of Energy Technology, Aalborg University. He was a Visiting Researcher at RWTH Aachen, Germany, in 2013. He has coauthored more than 70 journals and conference papers. His current research interests include energy storage systems for grid and e-mobility, Lithium-based batteries testing and modelling, and lifetime estimation of lithium-ion batteries.