State Estimation Strategies in Lithium-ion Battery Management Systems

Erscheinungsjahr 2023
ISBN: 978-0-443-16160-5
Verlag: Elsevier - Health Sciences Division

Buch, Englisch, 376 Seiten, Format (B × H): 230 mm x 152 mm, Gewicht: 608 g

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.

Autoren/Hrsg.

Liu, Kailong

Wang, Shunli

Wang, Yujie (Department of Automation

Stroe, Daniel-Ioan (Department of Energy Technology

Wang, Yujie

Fernandez, Carlos (Robert Gordon University

Stroe, Daniel-Ioan

Guerrero, Josep M. (Full

Fernandez, Carlos

Guerrero, Josep M.

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Energietechnik & Elektrotechnik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Energie- & Versorgungswirtschaft
• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde

Inhaltsverzeichnis

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

Über Autor(innen)

Guerrero, Josep M.
Josep M. Guerrero is a full professor with AAU Energy, Aalborg University, Denmark. He is the director of the Center for Research on Microgrids (CROM). He has published more than 800 journal articles in the fields of microgrids and renewable energy systems, which have been cited more than 80,000 times. His research interests focus on different microgrid aspects, including hierarchical and cooperative control, and energy management systems.

Stroe, Daniel-Ioan
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.

Wang, Shunli
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.

Wang, Yujie
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.

Fernandez, Carlos
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.

Liu, Kailong
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.