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Battery System Modeling
- 1st Edition - June 23, 2021
- 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
1. Li-ion Battery Characteristics and Applications
1.1Introduction to Li-ion Battery Technology
1.1.1Development History
1.1.2Energy Storage Technologies
1.2Battery Working Mechanism and Construction
1.2.1Characteristic Analysis
1.2.2Components and Working Principle
1.2.3Li-ion Battery Construction
1.2.4Charge-discharge Strategies
1.3Li-ion Battery Chemistries
1.3.1Li-ion Battery Family
1.3.2Battery with Different Materials
1.3.3Solid-state Li-ion Battery
1.3.4Comparative Battery Types Analysis
1.4Li-ion Battery Characteristics
1.4.1Internal Parameter Relationship
1.4.2Capacity Characteristics
1.4.3Open-circuit Voltage
1.4.4Internal Resistance Characteristic
1.4.5Power Capability Variation
1.4.6Coulombic Efficiency
1.5Battery Aging Behavior
1.5.1Aging Mechanisms
1.5.2Calendar Aging Process
1.5.3Cyclic Charge-discharge Aging
1.6Li-ion Battery Applications
1.6.1Applications
1.6.2System State Estimation
1.6.3Battery Safety Protection
1.6.4Battery Life Guarantee
1.6.5Status and Trends
1.7Conclusion
1.8Conflict of Interest
1.9Acknowledgement
REFERENCES
2. Electrical Equivalent Circuit Modeling
2.1Modeling Method Overview
2.1.1Modeling Types and Concepts
2.1.2Comparative Equivalent Models
2.1.3Commercial Circuit Models
2.1.4Electrochemical Model
2.1.5Equivalent Circuit Model
2.1.6Principle Description
2.1.7Parameter Identification
2.2Improved Internal Resistance Modeling
2.2.1Theoretical Resistance Modeling
2.2.2Battery Model Establishment
2.2.3Internal Resistance Description
2.2.4Open-circuit Voltage Characteristics
2.3Thevenin Modeling
2.3.1Construction of Thevenin Model
2.3.2Charge-discharge Characteristics
2.3.3State Equation Establishment
2.3.4Mathematical Description
2.4High Order Modeling
2.4.1Second-order Circuit Modeling
2.4.2Internal Resistance Description
2.4.3Splice Equivalent Modeling
2.5Parameter Identification Algorithms
2.5.1Identification Overview
2.5.2Least-square Functional Fitting
2.5.3Forgetting Factor Correction
2.6Experimental Analysis
2.6.1Exponential Curve Fitting
2.6.2Polynomial Relationship Description
2.6.3Identified Parameter Variation
2.6.4Pulse Voltage Tracking Effect
2.6.5Modeling Accuracy Verification
2.7Conclusion
2.8Conflict of Interest
2.9Acknowledgement
REFERENCES
3. Electrochemical Nernst Modeling
3.1Nernst Modeling and Improvement
3.1.1Model Building Process
3.1.2Parameter Identification Strategies
3.1.3State-space Description
3.2Modeling Realization
3.2.1Simulation Modeling Structure
3.2.2Characteristic Description
3.2.3Testing Procedure Design
3.3Model Parameter Identification
3.3.1Pulse Current Test Logic
3.3.2Parameter Identification Results
3.3.3Curve Fitting Analysis
3.3.4Simulation Result Analysis
3.4Experimental Verification
3.4.1Characteristic Testing
3.4.2Pulse-power Characteristic Test
3.4.3Varying Condition Voltage Tracking
3.4.4Modeling Result and Verification
3.5Conclusion
3.6Conflict of Interest
3.7Acknowledgement
REFERENCES
4. Battery State Estimation Methods
4.1State Parameter Identification
4.1.1State-of-charge Estimation
4.1.2State-of-energy Prediction
4.1.3State-of-power Evaluation
4.1.5Remaining Useful Life Prediction
4.2Battery State Influencing Factors
4.2.1Temperature Influence
4.2.2Charge-discharge Current Rate
4.2.3Self-discharging Description
4.2.4Aging Degree Variation
4.3Traditional State Estimation Methods
4.3.1Algorithm Comparison
4.3.2Foundational Methods
4.3.3Extended Kalman Filtering
4.3.4Particle Filtering Estimation
4.4Machine Learning Algorithms
4.4.1State of Art Introduction
4.4.2Support Vector Machine
4.4.3Self-learning Neural Network
4.4.4Deep Learning for Life Prediction
4.5Conclusion
4.6Conflict of Interest
4.7Acknowledgement
REFERENCES
5. Battery State-of-charge Estimation Methods
5.1Introduction
5.2State-of-charge Estimation Methods
5.2.1Coordinate Transformation
5.2.2Binary Iterative Algorithm
5.2.3Extended Kalman Filtering
5.2.4Algorithm Implementation
5.2.5Unscented Kalman Filtering
5.3Iterative Calculation and Modeling
5.3.1Equivalent Circuit Modeling
5.3.2Parameter Identification
5.3.3Kalman Filtering Algorithm
5.3.4Extended Taylor Series Expansion
5.3.5Estimation Model Construction
5.3.6Iterative Prediction and Correction
5.4Experimental Result Analysis
5.4.1Pulse-power Characteristic Test
5.4.2Estimation Features and Comparison
5.4.3Thermal Influencing Effect
5.4.4Time-varying Condition Influence
5.4.5Complex Current Rate Verification
5.5Conclusion
5.6Conflict of Interest
5.7Acknowledgement
REFERENCES
6. Battery State-of-energy Prediction Methods
6.1Overview
6.2Iterative Algorithm and Realization
6.2.1Equivalent Modeling
6.2.2Mathematical Description
6.2.3Iterative Calculation Procedure
6.2.4Parameter Initialization Strategy
6.2.5Estimation Model Construction
6.3Improved Prediction and Correction
6.3.1Cholesky Decomposition
6.3.2Calculation Algorithm Flow
6.3.3Covariance Matching
6.3.4Improved Correction Strategy
6.4Experimental Results Analysis
6.4.1Parameter Identification
6.4.2Pulse-power Characteristic Test
6.4.3Cyclic Intermittent Discharge
6.4.4Packing Pulse Current Test
6.4.5Estimation Processing Effect
6.5Conclusion
6.6Conflict of Interest
6.7Acknowledgement
REFERENCES
7. Battery State-of-power Evaluation Methods
7.1State-space Model Construction
7.2State Estimation Structural Design
7.2.1Algorithm Overview
7.2.2Iterative Calculation
7.3Calculation Procedure Design
7.3.1Computing Framework Design
7.3.2Iterative Calculation Steps
7.3.3Algorithm Improvement
7.3.4Estimation Modeling Realization
7.4Experimental Analysis
7.4.1Parameter Identification Results
7.4.2State Estimating and Voltage Tracking
7.4.3Power-temperature Variation
7.4.4Main Charge-Dischare Condition Test
7.4.5Pulse-current Charge-discharge Test
7.5Conclusion
7.6Conflict of Interest
7.7Acknowledgement
REFERENCES
8. Battery State-of-health Estimation Methods
8.1Equivalent Modeling and Description
8.1.1Equivalent Circuit Analysis
8.1.2Mathematical State-space Expression
8.2Particle Filtering Algorithm
8.2.1Bayesian Estimation
8.2.2Monte Carlo Treatment
8.2.3Importance Sampling
8.3Estimation Modeling Process
8.3.1Equivalent Circuit Modeling
8.3.2Calculation Process Design
8.3.3Particle Degradation Expression
8.3.4Re-sampling Treatment
8.4Whole Life-cycle Experiments
8.4.1Experimental Procedure Design
8.4.2Capacity Variation for New Batteries
8.4.3Character Test for New Batteries
8.4.4Aging Test for Pulse-current Cycles
8.4.5Capacity Variation for Aged Batteries
8.4.6Character Test for Aged Batteries
8.5Conclusion
8.6Conflict of Interest
8.7Acknowledgement
REFERENCES
9. Battery System Active Control Strategies
9.1Overview of Battery Management Systems
9.1.1Research Status
9.1.2Classification and Function
9.1.3Control System Design
9.2Charging Strategies for Capacity Extension
9.2.1Constant Current - Constant Voltage
9.2.2Multi-Stage Constant Current
9.2.3Pulse Current Charging
9.2.4Sinusoidal Ripple Current
9.2.5Experimental Analysis
9.3Balancing Control Methods
9.3.1Inconsistency Mechanism
9.3.2State-of-balance Description
9.3.3Balance Strategy Classification
9.3.4Passive Equalization
9.3.5Active Balancing Management
9.4Temperature Adjustment
9.4.1Overview of Thermal Controlling
9.4.2Air System Circulation Control
9.4.3Liquid Cooling and Heating
9.4.4Phase-change Heat Transfer
9.4.5Heat Pipe Temperature Control
9.4.6Heatable Thermal Management
9.5System Construction and Safety Control
9.5.1Overall Structure Design
9.5.2Core Factor Measurement
9.5.3System Protection
9.5.4Electrical Interface Connection
9.5.5Experimental Performance Test
9.6Conclusion
9.7Conflict of Interest
9.8Acknowledgement
REFERENCES
- No. of pages: 354
- Language: English
- Edition: 1
- Published: June 23, 2021
- Imprint: Elsevier
- Paperback ISBN: 9780323904728
- eBook ISBN: 9780323904339
SW
Shunli Wang
Shunli Wang Ph.D is a leading expert on new energy research, DTlab head, New energy measurement & control research team leader. Measurement & control processing is conducted on the needs of the high power Li-ion battery field for its modeling and state estimation strategy. More than 40 projects & 20 patents have been undertaken, publishing over 60 papers on world-famous journals such as Journal of Power Sources, obtaining 20 awards named as Science and Technology Progress Award and University & Enterprise Innovation Talent Team et al. Multiple generation systems have been developed for battery packs, improving the aircraft reliability and expanding its application fields with significant social and economic benefits.
Affiliations and expertise
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, 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