Smart Safety Management of Energy Storage Batteries

著者名：Wang PhD, Shunli/Xie PhD Student, Yanxin/Liu PhD, Guangchen/Huang PhD, Qi/Wang PhD, Yujie/Zhang PhD, Gexiang

Elsevier（2026/03/01発売）

• 言語：ENG
• ISBN：9780443275784
• eISBN：9780443275791

Description

Smart Safety Management of Energy Storage Batteries addresses battery management in new power systems which is an important component of the new generation of information technology and power systems. This book covers the application of this new type of power storage as well as power system identification modeling, intelligent energy storage battery status evaluation, and key technologies in intelligent management monitoring. Written for researchers, engineers, and students studying related areas, this book supports research in control science and control, automation, and electrical engineering, and serves as a technical reference for the application of new electric energy storage battery science and technology.- Contains technical references for system design and application- Addresses battery equivalent modeling, including electrical circuit modeling and parameter identification theory- Includes coverage of battery state estimation methods such as state of charge estimation, state of health estimation, and state-of-charge and state-of-health co-estimation

Table of Contents

1. Introduction2. Power storage battery testing3. Smart energy modeling analysis4. Intelligent core algorithm for predicting the state of energy storage batteries5. SOC estimation of energy storage battery based on LSTM neural network6. Data-driven state-of-health (SOH) estimation for energy storage battery clusters7. Battery peak power estimation based on long- and short-term memory networks8. Design and optimization of energy state assessment algorithms for energy storage batteries9. Improved firefly optimized method for lithium-ion batteries of the co-estimation of state of charge and state of health10. Joint estimation of state of charge and state of power for lithium-ion battery based on H∞ filtering11. Battery RUL prediction based on multicore correlation vector machine12. Intelligent balancing management of new power storage batteries