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Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs

By: Qi Huang,Shunli Wang,Zonghai Chen,Ran Xiong,Carlos Fernandez,Daniel-I. Stroe

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Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs

By: Qi Huang,Shunli Wang,Zonghai Chen,Ran Xiong,Carlos Fernandez,Daniel-I. Stroe

This book investigates in detail long-term health state estimation technology of energy storage systems, assessing its potential use to replace common filtering methods that constructs by equivalent circuit model with a data-driven method combined with electrochemical modeling, which can reflect the battery internal characteristics, the battery degradation modes, and the battery pack health state. Studies on long-term health state estimation have attracted engineers and scientists from various disciplines, such as electrical engineering, materials, automation, energy, and chemical engineering. Pursuing a holistic approach, the book establishes a fundamental framework for this topic, while emphasizing the importance of extraction for health indicators and the significant influence of electrochemical modeling and data-driven issues in the design and optimization of health state estimation in energy storage systems. The book is intended for undergraduate and graduate students who are interested in new energy measurement and control technology, researchers investigating energy storage systems, and structure/circuit design engineers working on energy storage cell and pack.

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Page Count

101

Publisher

Springer Nature

Published Date

ISBN 10

9819953448

ISBN 13

9789819953448

Battery System Modeling

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

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Battery System Modeling

By: 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. 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

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Page Count

356

Publisher

Elsevier

Published Date

ISBN 10

0323904335

ISBN 13

9780323904339