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      State Estimation Strategies in Lithium-ion Battery Management Systems

      AvKailong Liu,Yujie Wang

      Häftad, Engelska, 2023

      1 879 kr

      Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

      Beskrivning

      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

      Produktinformation

      • Utgivningsdatum:2023-07-20
      • Mått:152 x 229 x 23 mm
      • Vikt:590 g
      • Format:Häftad
      • Språk:Engelska
      • Antal sidor:376
      • Förlag:Elsevier Science
      • ISBN:9780443161605

      Utforska kategorier

      • Energiteknik inom Naturvetenskap och teknik
      • Energiindustri inom Ekonomi och Ledarskap

      Mer om författaren

      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. 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. Josep M. Guerrero is Director, Center for Research on Microgrids (CROM), Huanjiang Laboratory, Zhejiang University, Zhuji, China and a full professor with AAU Energy, Aalborg University, Denmark. He is the director of the Center for Research on Microgrids (CROM). His research interests focus on different microgrid aspects, including hierarchical and cooperative control, and energy management systems. Shunli Wang is a professor and doctoral supervisor, Executive Vice President of Smart Energy Storage Institute, Academic Dean of Electric Power College at Inner Mongolia University of Technology, Academic Member of Beijing Innovation Society, Deputy Director of Energy Storage Technology Center of National Joint Laboratory of Renewable Energy, Deputy General Manager of Daqingshan Laboratory of Inner Mongolia Electric Power Group, Visiting Professor/Doctoral Director of Xi'an Jiaotong Liverpool University, Academician of Russian Academy of Natural Sciences, IETFlow, Academic Leader of the National Electrical Safety and Quality Testing Center, Provincial Qingcheng Tianfu Scientific and Technological Talents, Provincial high-level overseas talents, provincial Class A Tianfu talents, the first batch of academic and technical leaders in China Science and Technology City, and the top 2% of the world's top scientists. His research interests include modeling, state estimation, and safety management for energy storage systems. 56 projects have been undertaken, supported by the National Natural Science Foundation of China and the Provincial Science and Technology Department, et al. 63 intellectual property rights have been approved. 9 monographs have been published by famous publishers of Elsevier and IET, and so on. He has won 9 awards at provincial and ministerial levels, including 3 international gold medals, 17 international conference chairmen, 5 editorial boards of international and domestic Chinese journals, a core technical achievements appraisal reaching an international advanced level, reported by the People's Daily.

      Innehållsförteckning

      • 1. Introduction to current research in estimation strategies and prediction algorithms 2. Characteristic analysis of power lithium-ion batteries 3. Aging characteristics of lithium-ion batteries 4. Lithium-ion battery hysteresis characteristics and modeling 5. Lithium-ion battery aging mechanism and multiple regression model 6. Equivalent modeling and parameter identification of power lithium-ion batteries 7. Equivalent modeling study of aviation lithium-ion batteries 8. Battery SOC measurement and control model based on Internet platforms 9. High energy density lithium-ion battery SOC prognosis 10. SOC estimation strategy based on fractional-order model 11. SOC estimation method for large unmanned aerial vehicles 12. Construction of SOC estimation method for automotive ternary batteries 13. Estimation strategies for SOC and SOP of lithium-ion batteries 14. Collaborative energy and peak power status estimation 15. SOH estimation based on improved double-extended Kalman filter 16. Collaborative SOC and SOH estimation based on improved AUKF-UPF algorithm
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