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Long-term health state estimation of energy storage lithium-ion battery packs / Qi Huang, Shunli Wang, Zonghai Chen, Ran Xiong, Carlos Fernandez, Daniel-I. Stroe.

9789819953448 (electronic bk.)
9819953448 (electronic bk.)
9789819953431
981995343X

Singapore : Springer, [2023]

English

1 online resource (xi, 92 pages) : illustrations (some color)

10.1007/978-981-99-5344-8 doi

TK2945.L58 H83 2023

621.31/2424

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.

Includes bibliographical references.

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Online resource; title from PDF title page (SpringerLink, viewed August 24, 2023).

Print version: 9789819953431

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