| 书目名称 | Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs | | 编辑 | Qi Huang,Shunli Wang,Daniel-I. Stroe | | 视频video | | | 概述 | Develops novel battery health state estimation methods of energy storage systems.Introduces methods of battery degradation modes, including loss of active material and lithium inventory quantification | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Book 2023 | | 关键词 | Energy storage; Lithium-ion battery; Battery health state; Multi-cell model of battery pack; Back propag | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-99-5344-8 | | isbn_softcover | 978-981-99-5343-1 | | isbn_ebook | 978-981-99-5344-8 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |
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发表于 2025-3-21 17:39:33
