Modelling, Estimation and AI applications for Lithium-Ion Battery Management Systems
- 1st Edition - September 1, 2026
- Latest edition
- Editors: Shunli Wang, Qi Huang, Liya Zhang, Guangchen Liu, Carlos Fernandez, Frede Blaabjerg
- Language: English
Modelling, Estimation and AI applications for Lithium-Ion Battery Management Systems is comprehensive guide to the latest advancements in integrating artificial intelligence with l… Read more
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Modelling, Estimation and AI applications for Lithium-Ion Battery Management Systems is comprehensive guide to the latest advancements in integrating artificial intelligence with lithium-ion battery technology. As the world accelerates toward carbon neutrality and a sustainable energy future, lithium-ion batteries play a pivotal role in electric transportation and energy storage systems. This book offers an in-depth exploration of fundamental principles, advanced modeling techniques, and state estimation strategies vital for enhancing battery performance, safety, and longevity. The book has a systematic coverage of battery operation, performance testing methods, and application scenarios, providing a solid foundation for understanding current challenges and innovations. The book delves into core AI algorithms, including machine learning, deep learning, and hybrid approaches, illustrating how they revolutionize battery modeling and health monitoring. Key topics include hybrid modeling methods that combine equivalent circuit models, electrochemical theories, and AI techniques; precise estimation of State of Charge (SOC), State of Health (SOH), and State of Power (SOP); and strategies for joint state estimation to facilitate comprehensive battery management. Practical insights are reinforced with detailed discussions on experimental platform design, validation procedures, and data visualization techniques, bridging theory and real-world engineering.
Ideal for researchers, engineers, and practitioners in battery technology, energy storage, and intelligent energy systems, this book equips readers with the latest methodologies and trends to advance sustainable energy solutions. Whether you're developing next-generation batteries or optimizing existing systems, this authoritative resource will guide you through the innovative landscape of AI-powered battery management.
Ideal for researchers, engineers, and practitioners in battery technology, energy storage, and intelligent energy systems, this book equips readers with the latest methodologies and trends to advance sustainable energy solutions. Whether you're developing next-generation batteries or optimizing existing systems, this authoritative resource will guide you through the innovative landscape of AI-powered battery management.
- Utilizes machine learning and deep learning as the core driving force, integrating traditional equivalent circuit models with data-driven models to overcome the limitations of single physical modeling or scenario constraints
- Introduces the SOP (State-Of-Power) Estimation and multi-state joint optimization framework to address coupling defects inherent in traditional independent estimation methods
- Teaches to build a comprehensive chain encompassing “AI algorithm foundation — multi-model fusion — full life cycle management — experimental verification” to ensure robustness and reliability of Li-ion batteries
Electric energy storage researchers, Battery researchers, and Related teachers; Master’s and PhD students doing research in battery management systems, AI based diagnostics systems and power systems; Engineering or Applied Sciences students discovering to bridge AI and electrochemical systems
1. Overview and Applications of Lithium-ion Batteries
1.1 Operating principles of lithium-ion batteries
1.2 Performance testing methods
1.2.1 Energy test experiments
1.2.2 Hybrid pulse power characterization
1.2.3 Charge-discharge tests under various C-rates
1.2.4 Capacity calibration experiments
1.2.5 Aging tests
1.3 Performance characteristics
1.3.1 Basic electrochemical properties
1.3.2 Dynamic response characteristics
1.3.3 Temperature dependence
1.3.4 Aging behavior
1.3.5 Safety boundary characteristics
1.4 Application scenarios
1.4.1 Electric vehicles
1.4.2 Energy storage systems
1.4.3 Portable electronic devices
1.4.4 Aerospace and military
1.4.5 Industrial automation and robotics
2. Fundamentals of Artificial Intelligence and Algorithms
2.1 Basics of machine learning
2.1.1 Principles of supervised learning
2.1.2 Overview of unsupervised learning
2.1.3 Fundamentals of reinforcement learning
2.2 Battery Data Types and Preprocessing for Intelligent Modeling
2.2.1 Input Features: Voltage, Current, Temperature
2.2.2 Denoising and outlier handling
2.2.3 Data standardization and normalization
2.2.4 Data enhancement and balance
2.2.5 Time series alignment and windowing
2.3 Fundamentals of deep learning
2.3.1 Artificial neural network structures
2.3.2 Convolutional neural networks (CNN)
2.3.3 Recurrent neural networks (RNN) and LSTM
2.4 Other AI techniques 2.4.1 Fuzzy logic and expert systems
2.4.2 Particle swarm optimization
2.4.3 Introduction to genetic algorithms
2.5 Trends in AI applications
2.5.1 Multi-state coupling modeling
2.5.2 Product life-cycle management
2.5.3 Physics-informed AI
3. Modeling Methods for Lithium-ion Batteries
3.1 Equivalent circuit models
3.1.1 Model structures
3.1.2 Parameter identification
3.1.3 Applications and improvements
3.2 Electrochemical models
3.2.1 Theoretical foundations
3.2.2 Porous electrode theory
3.2.3 Numerical solution methods
3.3 AI-based modeling approaches
3.3.1 Limitations of traditional models
3.3.2 Machine learning for battery modeling
3.3.3 Deep learning-based modeling techniques
3.3.4 From physical to hybrid modeling
4. State of charge (SOC) estimation
4.1 Definition and significance
4.2 Traditional SOC estimation methods
4.3 SOC estimation based on CKF and improved algorithms
4.4 SOC estimation based on CNN and improved algorithms
4.5 Whole-life-cycle remaining capacity estimation
5. State of health (SOH) estimation
5.1 Definition and significance
5.2 Empirical models from aging data
5.3 Machine learning-based SOH estimation
5.4 Advances in deep learning for SOH prediction
5.5 Whole-life-cycle remaining capacity estimation
6. State of power (SOP) estimation
6.1 Definition and significance
6.2 SOP estimation based on UKF and improved algorithms 6.3 SOP estimation based on LSTM and improved algorithms 6.4 Multi-factor coupled SOP strategies
7. Joint Estimation of Battery States
7.1 Theoretical background
7.1.1 Coupled characteristics of battery states
7.1.2 Limitations of independent estimation
7.1.3 Full lifecycle health management
7.2 Traditional joint estimation methods
7.2.1 Extended Kalman filter-based methods
7.2.2 Particle filter-based methods
7.3 AI-based joint estimation approaches
7.3.1 Gaussian process regression
7.3.2 Support vector machines
7.3.3 Bayesian inference methods
8. Experimental Design and Validation
8.1 Development of experimental platform
8.1.1 Electrochemical Impedance Spectroscopy Instruments
8.1.2 High-Precision Charge–Discharge Testing Systems
8.1.3 Temperature-Controlled Environmental Chambers
8.2 Design of test protocols under varying conditions
8.2.1 Hybrid Pulse Power Characterization (HPPC) Protocol
8.2.2 Bursts of Balanced Dynamic Stress Test (BBDST) Protocol
8.2.3 Dynamic Stress Test (DST) Protocol
8.3 Visualization and analysis of experimental data
8.3.1 Data Cleaning and Preprocessing Techniques
8.3.2 Multi-Dimensional Data Visualization Approaches
8.3.3 Visualization Tools and Application Scenarios
9. Future Directions and Societal Impact
9.1 Technical future directions in battery management
9.1.1 AI-Driven advanced modeling
9.1.2 Adaptive parameter reconstruction
9.1.3 Real-time learning and edge intelligence collaboration
9.2 Societal impact and industrial transformation
9.2.1 Economic impacts on energy ecosystems
9.2.2 Environmental and sustainability implications
9.2.3 Promoting the dual-carbon goals and the transition to green energy
9.3 Challenges and mitigation strategies
9.3.1 Limited model generalization and challenging transferability
9.3.2 Underdeveloped continual learning mechanisms
1.1 Operating principles of lithium-ion batteries
1.2 Performance testing methods
1.2.1 Energy test experiments
1.2.2 Hybrid pulse power characterization
1.2.3 Charge-discharge tests under various C-rates
1.2.4 Capacity calibration experiments
1.2.5 Aging tests
1.3 Performance characteristics
1.3.1 Basic electrochemical properties
1.3.2 Dynamic response characteristics
1.3.3 Temperature dependence
1.3.4 Aging behavior
1.3.5 Safety boundary characteristics
1.4 Application scenarios
1.4.1 Electric vehicles
1.4.2 Energy storage systems
1.4.3 Portable electronic devices
1.4.4 Aerospace and military
1.4.5 Industrial automation and robotics
2. Fundamentals of Artificial Intelligence and Algorithms
2.1 Basics of machine learning
2.1.1 Principles of supervised learning
2.1.2 Overview of unsupervised learning
2.1.3 Fundamentals of reinforcement learning
2.2 Battery Data Types and Preprocessing for Intelligent Modeling
2.2.1 Input Features: Voltage, Current, Temperature
2.2.2 Denoising and outlier handling
2.2.3 Data standardization and normalization
2.2.4 Data enhancement and balance
2.2.5 Time series alignment and windowing
2.3 Fundamentals of deep learning
2.3.1 Artificial neural network structures
2.3.2 Convolutional neural networks (CNN)
2.3.3 Recurrent neural networks (RNN) and LSTM
2.4 Other AI techniques 2.4.1 Fuzzy logic and expert systems
2.4.2 Particle swarm optimization
2.4.3 Introduction to genetic algorithms
2.5 Trends in AI applications
2.5.1 Multi-state coupling modeling
2.5.2 Product life-cycle management
2.5.3 Physics-informed AI
3. Modeling Methods for Lithium-ion Batteries
3.1 Equivalent circuit models
3.1.1 Model structures
3.1.2 Parameter identification
3.1.3 Applications and improvements
3.2 Electrochemical models
3.2.1 Theoretical foundations
3.2.2 Porous electrode theory
3.2.3 Numerical solution methods
3.3 AI-based modeling approaches
3.3.1 Limitations of traditional models
3.3.2 Machine learning for battery modeling
3.3.3 Deep learning-based modeling techniques
3.3.4 From physical to hybrid modeling
4. State of charge (SOC) estimation
4.1 Definition and significance
4.2 Traditional SOC estimation methods
4.3 SOC estimation based on CKF and improved algorithms
4.4 SOC estimation based on CNN and improved algorithms
4.5 Whole-life-cycle remaining capacity estimation
5. State of health (SOH) estimation
5.1 Definition and significance
5.2 Empirical models from aging data
5.3 Machine learning-based SOH estimation
5.4 Advances in deep learning for SOH prediction
5.5 Whole-life-cycle remaining capacity estimation
6. State of power (SOP) estimation
6.1 Definition and significance
6.2 SOP estimation based on UKF and improved algorithms 6.3 SOP estimation based on LSTM and improved algorithms 6.4 Multi-factor coupled SOP strategies
7. Joint Estimation of Battery States
7.1 Theoretical background
7.1.1 Coupled characteristics of battery states
7.1.2 Limitations of independent estimation
7.1.3 Full lifecycle health management
7.2 Traditional joint estimation methods
7.2.1 Extended Kalman filter-based methods
7.2.2 Particle filter-based methods
7.3 AI-based joint estimation approaches
7.3.1 Gaussian process regression
7.3.2 Support vector machines
7.3.3 Bayesian inference methods
8. Experimental Design and Validation
8.1 Development of experimental platform
8.1.1 Electrochemical Impedance Spectroscopy Instruments
8.1.2 High-Precision Charge–Discharge Testing Systems
8.1.3 Temperature-Controlled Environmental Chambers
8.2 Design of test protocols under varying conditions
8.2.1 Hybrid Pulse Power Characterization (HPPC) Protocol
8.2.2 Bursts of Balanced Dynamic Stress Test (BBDST) Protocol
8.2.3 Dynamic Stress Test (DST) Protocol
8.3 Visualization and analysis of experimental data
8.3.1 Data Cleaning and Preprocessing Techniques
8.3.2 Multi-Dimensional Data Visualization Approaches
8.3.3 Visualization Tools and Application Scenarios
9. Future Directions and Societal Impact
9.1 Technical future directions in battery management
9.1.1 AI-Driven advanced modeling
9.1.2 Adaptive parameter reconstruction
9.1.3 Real-time learning and edge intelligence collaboration
9.2 Societal impact and industrial transformation
9.2.1 Economic impacts on energy ecosystems
9.2.2 Environmental and sustainability implications
9.2.3 Promoting the dual-carbon goals and the transition to green energy
9.3 Challenges and mitigation strategies
9.3.1 Limited model generalization and challenging transferability
9.3.2 Underdeveloped continual learning mechanisms
- Edition: 1
- Latest edition
- Published: September 1, 2026
- Language: English
SW
Shunli Wang
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.
Affiliations and expertise
Southwest University of Science and Technology, ChinaQH
Qi Huang
Huang Qi is a professor and doctoral supervisor, IEEE Fellow, IETF fellow, expert enjoying special government allowance of the State Council, candidate of New Century Excellent Talents Support Program of Ministry of Education, academic and technical leader of Sichuan Province, head of Youth Science and Technology Innovation Team of Sichuan Province, candidate of "Tianfu Qingcheng Innovation Leading Talents Program" of Sichuan Province. His main research fields are: new power energy systems, science and technology innovation strategy, and development planning etc. He has undertaken more than 20 national and provincial projects such as National Key R & D Plan and National Natural Science Foundation, and won 1 China Patent Excellence Award, 1 First Prize of China Instrument Society, 1 First Prize/Second Prize of Sichuan Province Science and Technology Progress Award, and 1 Second Prize of Excellent Scientific and Technological Achievements of Ministry of Education. He has published more than 300 academic papers, including more than 200 SCI papers, published 5 monographs, including 2 Wiley-IEEE monographs, applied for more than 100 patents, and obtained more than 80 authorized national invention patents and 2 US patents. He served as a member of the Expert Group on Science and Technology Innovation Planning in the Energy Field of the 13th Five-Year Plan of the Ministry of Science and Technology, the leader of the Expert Group on the 14th Five-Year Plan in the Energy and Chemical Industry Field of Sichuan Province, the convener of the 13th Five-Year Plan in the New Energy Field of Sichuan Province, and the leader of the Expert Group on "Clean Energy" in the "5+1" Modern Industrial System of Sichuan Province. He served as the chairman of several high-level international conferences, such as the 2019 IEEPES Innovative Smart Grid Technology and the 2022 IEEEI 2.
Affiliations and expertise
President, Southwest University of Science and Technology, ChinaLZ
Liya Zhang
Liya Zhang is a Doctoral Student at Inner Mongolia University of Technology. As a key member, she participated in 5 major research projects, including the National Key Research and Development Program and the National Natural Science Foundation. Authorized 2 domestic invention patents and won the Gold Award at the BRICS Invention Exhibition.
Affiliations and expertise
Doctoral Student, Inner Mongolia University of Technology, ChinaGL
Guangchen Liu
Guangchen Liu, Professor at Inner Mongolia University of Technology. Mainly engaged in teaching and research work in the fields of new energy generation and energy storage, power electronics, and power transmission. Hosted multiple projects such as the National Natural Science Foundation, Inner Mongolia Science and Technology Major Special Project, and Inner Mongolia Science and Technology Plan, published over 70 academic papers, and authored 3 monographs/textbooks. The research achievements are mainly used to solve the problem of high-quality power supply in remote areas.
Affiliations and expertise
Professor, Inner Mongolia University of 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
Associate Professor, Robert Gordon University, Scotland, UKFB
Frede Blaabjerg
Frede Blaabjerg's current research interests include power electronics and its applications, such as in wind turbines, PV systems, reliability, Power-2-X, power quality, and adjustable speed drives. He has published more than 900 journal papers in the fields of power electronics and its applications. He is the co-author of ten monographs and editor of twenty books in power electronics and its applications eg. the series (4 volumes) Control of Power Electronic Converters and Systems published by Academic Press/Elsevier.
Affiliations and expertise
Professor of Power Electronic Systems, Department of Energy, Technology, Aalborg University, Denmark