State Estimation Strategies in Lithium-ion Battery Management Systems
- 1st Edition - July 14, 2023
- Imprint: Elsevier
- Authors: Kailong Liu, Yujie Wang, Daniel-Ioan Stroe, Carlos Fernandez, Josep M. Guerrero, Shunli Wang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 6 1 6 0 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 6 1 6 1 - 2
State Estimation Strategies in Lithium-ion Battery Management Systems presents key technologies and methodologies in modeling and monitoring charge, energy, power and health of… Read more
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Request a sales quoteState 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
Academia: Researchers, advanced students, and scientists in energy storage, control, automation, electrical engineering, power systems, materials science, and chemical engineering. Industry: Engineers, R&D professionals, and other industry personnel with an interest in lithium-ion batteries, battery modelling, SOC estimation, energy management, and energy storage
- Cover Image
- Title page
- Table of Contents
- Copyright
- Preface
- Chapter 1. Introduction
- Abstract
- 1.1 Research background
- 1.2 Research significance
- 1.3 Research status
- References
- Chapter 2. Characteristic analysis of power lithium-ion batteries
- Abstract
- 2.1 Research background
- 2.2 Working characteristic analysis of lithium-ion batteries
- 2.3 Chapter summary
- References
- Chapter 3. Aging characteristics of lithium-ion batteries
- Abstract
- 3.1 Basic characteristics of lithium-ion batteries
- 3.2 Analysis of state of charge affecting factors
- 3.3 Battery aging characteristic analysis
- 3.4 Chapter summary
- References
- Chapter 4. Lithium-ion battery hysteresis characteristics and modeling
- Abstract
- 4.1 Hysteresis characteristics of lithium-ion battery
- 4.2 Lithium-ion battery open-circuit voltage-state of charge model
- 4.3 Chapter summary
- References
- Chapter 5. Lithium-ion battery aging mechanism and multiple regression model
- Abstract
- 5.1 Research background
- 5.2 Study on the changing pattern of aging characteristic parameters
- 5.3 State-of-health estimation model
- 5.4 Chapter summary
- References
- Chapter 6. Equivalent modeling and parameter identification of power lithium-ion batteries
- Abstract
- 6.1 Modeling of power lithium-ion battery
- 6.2 Parameter identification of battery model
- 6.3 Full-parameter online identification with real-time data tracking
- 6.4 Parameter identification and model verification
- 6.5 Chapter summary
- References
- Chapter 7. Equivalent modeling study of aviation lithium-ion batteries
- Abstract
- 7.1 Equivalent modeling and state-space equation
- 7.2 Parameter identification methods for improving the M-Thevenin model
- 7.3 Parameter identification results and equivalent model characterization effect analysis
- 7.4 Chapter summary
- References
- Chapter 8. Battery state-of-charge measurement and control model based on the Internet platform
- Abstract
- 8.1 Overall scheme design of internet-based battery state-of-charge estimation
- 8.2 State-of-charge estimation model based on back propogation neural network
- 8.3 State-of-charge estimation model based on NARX neural network
- 8.4 State-of-charge estimation model based on NARX neural network and AEKF
- 8.5 Summary
- References
- Chapter 9. High energy density lithium-ion battery state of charge prognosis
- Abstract
- 9.1 Battery state of charge estimation strategy analysis
- 9.2 Bayesian filtering and its derivative algorithm research and improvement
- 9.3 Iterative estimation of battery state of charge with multi-influence correction
- 9.4 State of charge estimation results and analysis under typical operating conditions
- 9.5 Summary
- References
- Chapter 10. State of charge estimation strategy based on fractional-order model
- Abstract
- 10.1 Extended Kalman filtering for state of charge estimation
- 10.2 Adaptive fractional-order extended Kalman filtering algorithm
- 10.3 Summary
- References
- Chapter 11. State-of-charge estimation method for large unmanned aerial vehicle
- Abstract
- 11.1 Kalman filter algorithm
- 11.2 Improvement of nonlinear Kalman filter algorithm application
- 11.3 Improvement and calculation process optimization based on the extended Kalman filter algorithm
- 11.4 State-of-charge estimation accuracy analysis under unmanned aerial vehicles customized conditions
- 11.5 Summary
- References
- Chapter 12. Construction of state of charge estimation method for automotive ternary batteries
- Abstract
- 12.1 Kalman filtering and iterative operations of extension algorithm
- 12.2 State of charge estimation based on square root unscented Kalman filter algorithm
- 12.3 Original square root unscented Kalman filter algorithm for dual online iterative system
- 12.4 Constant current discharge and state of charge estimation under dynamic stress test conditions
- 12.5 Battery state of charge estimation under complex operating conditions
- 12.6 Summary
- References
- Chapter 13. Estimation strategies for state of charge and state of power of lithium-ion batteries
- Abstract
- 13.1 State of charge estimation strategy based on square root unscented Kalman filter algorithm
- 13.2 State of charge evaluation strategy validation analysis
- 13.3 State of power estimation strategy under multiple constraints
- 13.4 State of power evaluation strategy analysis
- 13.5 Summary
- References
- Chapter 14. Collaborative energy and peak power status estimation
- Abstract
- 14.1 Development of estimation models
- 14.2 Summary
- References
- Chapter 15. State of health estimation based on improved double-extended Kalman filter
- Abstract
- 15.1 State of health estimation based on adaptive extended Kalman filter
- 15.2 State of health estimation based on dual time scale adaptive double extended kalman filter (ADEKF) algorithm
- 15.3 State of health estimation method based on the fusion of two factors
- 15.4 Effect analysis of aviation lithium-ion battery state estimation
- 15.5 Chapter summary
- References
- Chapter 16. Collaborative state of charge and state of health estimation based on improved adaptive unscented Kalman-unscented particle filter algorithm
- Abstract
- 16.1 Battery state of health estimation strategy analysis
- 16.2 Battery state of health estimation strategy analysis
- 16.3 Collaborative state of charge and state of health estimation based on different iteration periods
- 16.4 State of health estimation results and analysis under typical working conditions
- 16.5 Summary
- References
- Index
- Edition: 1
- Published: July 14, 2023
- No. of pages (Paperback): 376
- No. of pages (eBook): 376
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780443161605
- eBook ISBN: 9780443161612
KL
Kailong Liu
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.
YW
Yujie Wang
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.
Affiliations and expertise
Department of Automation, University of Science and Technology of China, ChinaDS
Daniel-Ioan Stroe
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.
Affiliations and expertise
Department of Energy Technology, Aalborg University, Denmark Department of Energy Technology, Aalborg University, DenmarkCF
Carlos Fernandez
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.
Affiliations and expertise
Robert Gordon University, Aberdeen, UKJG
Josep M. Guerrero
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.
Affiliations and expertise
Full Professor, AAU Energy, Aalborg University and Director of the Center for Research on Microgrids (CROM), DenmarkSW
Shunli Wang
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.
Affiliations and expertise
Southwest University of Science and Technology, ChinaRead State Estimation Strategies in Lithium-ion Battery Management Systems on ScienceDirect