Battery System Modeling
- 1st Edition - June 23, 2021
- Imprint: Elsevier
- Authors: Shunli Wang, Carlos Fernandez, Yu Chunmei, Yongcun Fan, Cao Wen, Daniel-Ioan Stroe, Zonghai Chen
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 4 7 2 - 8
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 4 3 3 - 9
Battery System Modeling provides advances on the modeling of lithium-ion batteries. Offering step-by-step explanations, the book systematically guides the reader through the model… Read more
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Request a sales quoteBattery System Modeling provides advances on the modeling of lithium-ion batteries. Offering step-by-step explanations, the book systematically guides the reader through the modeling of state of charge estimation, energy prediction, power evaluation, health estimation, and active control strategies. Using applications alongside practical case studies, each chapter shows the reader how to use the modeling tools provided. Moreover, the chemistry and characteristics are described in detail, with algorithms provided in every chapter. Providing a technical reference on the design and application of Li-ion battery management systems, this book is an ideal reference for researchers involved in batteries and energy storage.
Moreover, the step-by-step guidance and comprehensive introduction to the topic makes it accessible to audiences of all levels, from experienced engineers to graduates.
- Explains how to model battery systems, including equivalent, electrical circuit and electrochemical nernst modeling
- Includes comprehensive coverage of battery state estimation methods, including state of charge estimation, energy prediction, power evaluation and health estimation
- Provides a dedicated chapter on active control strategies
Researchers and engineers of all levels of experience working on energy storage and batteries, specifically those related to lithium ion batteries, energy storage devices, and battery management systems. Graduate students and industry engineers involved in lithium ion batteries, energy storage devices, and battery management systems
- Cover image
- Title page
- Table of Contents
- Copyright
- Chapter 1: Lithium-ion battery characteristics and applications
- Abstract
- 1.1: Introduction to lithium-ion battery technology
- 1.2: Battery working mechanism
- 1.3: Lithium-ion battery chemistries
- 1.4: Lithium-ion battery characteristics
- 1.5: Battery aging behavior
- 1.6: Lithium-ion battery applications
- 1.7: Conclusion
- Chapter 2: Electrical equivalent circuit modeling
- Abstract
- 2.1: Modeling method overview
- 2.2: Improved internal resistance modeling
- 2.3: Thevenin modeling
- 2.4: High-order modeling
- 2.5: Parameter identification algorithms
- 2.6: Experimental analysis
- 2.7: Conclusion
- Chapter 3: Electrochemical Nernst modeling
- Abstract
- 3.1: Nernst modeling and improvement
- 3.2: Modeling realization
- 3.3: Model parameter identification
- 3.4: Experimental verification
- 3.5: Conclusion
- Chapter 4: Battery state estimation methods
- Abstract
- 4.1: State parameter identification
- 4.2: Battery state influencing factors
- 4.3: Traditional state estimation methods
- 4.4: Machine learning algorithms
- 4.5: Conclusion
- Chapter 5: Battery state-of-charge estimation methods
- Abstract
- 5.1: Introduction
- 5.2: State-of-charge estimation methods
- 5.3: Iterative calculation and modeling
- 5.4: Experimental result analysis
- 5.5: Conclusion
- Chapter 6: Battery state-of-energy prediction methods
- Abstract
- 6.1: Overview
- 6.2: Iterative algorithm and realization
- 6.3: Improved prediction and correction
- 6.4: Experimental results analysis
- 6.5: Conclusion
- Chapter 7: Battery state-of-power evaluation methods
- Abstract
- 7.1: State-space model construction
- 7.2: State estimation structural design
- 7.3: Calculation procedure design
- 7.4: Experimental analysis
- 7.5: Conclusion
- Chapter 8: Battery state-of-health estimation methods
- Abstract
- 8.1: Equivalent modeling and description
- 8.2: Particle filtering algorithm
- 8.3: Estimation modeling process
- 8.4: Whole life-cycle experiments
- 8.5: Conclusion
- Chapter 9: Battery system active control strategies
- Abstract
- 9.1: Overview of battery management systems
- 9.2: Charging strategies for capacity extension
- 9.3: Balancing control methods
- 9.4: Temperature adjustment
- 9.5: Conclusion
- Index
- Edition: 1
- Published: June 23, 2021
- Imprint: Elsevier
- No. of pages: 354
- Language: English
- Paperback ISBN: 9780323904728
- eBook ISBN: 9780323904339
SW
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, ChinaCF
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, UKYC
Yu Chunmei
Yu Chunmei Ph.D was born in Rugao in Jiangsu Province, being interested in state estimation, system identification, and fault diagnosis. Teaching courses as automatic control theory, system identification, and modeling, etc. for undergraduate and postgraduate students. More than 10 research projects have participated in the recent 5 years, such as the Natural science funding, the Provincial Department of Science and Technology, and projects from enterprises. More than 30 papers have been published on various kinds of worldwide academic journals.
Affiliations and expertise
School of Information Engineering, Southwest University of Science and Technology, ChinaYF
Yongcun Fan
Fan Yongcun Ph.D is a core member of the new energy measurement and control research team. Focusing on the measurement and control needs of the new energy field, signal detection and state estimation, anti-interference processing, and control strategy research are carried out to explore the state detection and control theory.
Affiliations and expertise
School of Information Engineering, Southwest University of Science and Technology, ChinaCW
Cao Wen
Cao Wen Ph.D main research is based on the battery measurement and control technology, the research of charging algorithms, sensors, and the experimental test device is carried out. In the past five years, he has undertaken more than 10 scientific research projects of the Ministry of education of China and the science and Technology Department of Sichuan Province and published more than 20 research papers together with lab members.
Affiliations and expertise
School of Information Engineering, Southwest University of Science and Technology, 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, DenmarkZC
Zonghai Chen
Zonghai Chen Ph.D was born in Anhui, China, in December 1963. He received the B.S. and M.E. degrees from the University of Science and Technology of China (USTC), Hefei, China, in 1988 and 1991, respectively. He has been a Professor with the Department of Automation, USTC, since 1998. His main research interests include modeling and control of complex systems, intelligent robotic and information processing, energy management technologies for electric vehicles, and smart microgrids. Prof. Chen is a recipient of special allowances from the State Council of China. He is a member of the Robotics Technical Committee and Modelling, Identification and Signal Processing Technical Committee of the International Federation of Automation Control.
Affiliations and expertise
Department of Automation, University of Science and Technology of China, ChinaRead Battery System Modeling on ScienceDirect