TY - BOOK
T1 - Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs
AU - Huang, Qi
AU - Wang, Shunli
AU - Chen, Zonghai
AU - Xiong, Ran
AU - Fernandez, Carlos
AU - Stroe, Daniel-Ioan
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Energy storage
KW - Lithium-ion battery
KW - Battery health state
KW - Multi-cell model of battery pack
KW - Back propagataion neural network
KW - Electrochemical model
KW - Machine learning
KW - Battery characteristics
KW - Extended single particle model
KW - Parameter identification
KW - Degradation mode
KW - Data-driven model
U2 - 10.1007/978-981-99-5344-8
DO - 10.1007/978-981-99-5344-8
M3 - Book
SN - 978-981-99-5343-1
BT - Long-Term Health State Estimation of Energy Storage Lithium-Ion Battery Packs
PB - Springer
ER -