Battery System Modeling 1st edition by Shunli Wang, Carlos Fernandez, Yu Chunmei, Yongcun Fan, Cao Wen, Daniel Ioan Stroe, Zonghai Chen 0323904339 9780323904339

Battery System Modeling 1st edition by Shunli Wang, Carlos Fernandez, Yu Chunmei, Yongcun Fan, Cao Wen, Daniel-Ioan Stroe, Zonghai Chen – Ebook PDF Instant Download/DeliveryISBN:  0323904339, 9780323904339 

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Product details:

ISBN-10 :  0323904339 

ISBN-13 :  9780323904339

Author: Shunli Wang, Carlos Fernandez, Yu Chunmei, Yongcun Fan, Cao Wen, Daniel-Ioan Stroe, Zonghai Chen 

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 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.

Battery System Modeling 1st Table of contents:

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

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Tags: Battery System, Modeling, Shunli Wang, Carlos Fernandez, Yu Chunmei, Yongcun Fan, Cao Wen, Daniel Ioan Stroe, Zonghai Chen