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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Power storage battery testing - p. 33 3. Chapter 3 Smart energy modeling analysis - p. 55 4. Chapter 4 Intelligent core algorithm for predicting the state of energy storage batteries - p. 95 5. Chapter 5 State of charge estimation of energy storage battery based on long short-term memory neural network - p. 139 6. Chapter 6 Data-driven state of health estimation for energy storage battery clusters - p. 175 7. Chapter 7 Battery peak power estimation based on long- and short-term memory networks - p. 239 8. Chapter 8 Design and optimization of energy state assessment algorithms for energy storage batteries - p. 261 9. Chapter 9 Improved firefly optimized method for lithium-ion batteries of the co-estimation of state of charge and state of health - p. 297 10. Chapter 10 Joint estimation of state of charge and state of power for lithium-ion battery based on H-infinity filtering - p. 341 11. Chapter 11 Battery remaining useful life prediction based on multicore correlation vector machine - p. 381 12. Chapter 12 Intelligent balancing management of new power storage batteries - p. 433 13. References - p. 461 14. Index - p. 489
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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Power storage battery testing - p. 33 3. Chapter 3 Smart energy modeling analysis - p. 55 4. Chapter 4 Intelligent core algorithm for predicting the state of energy storage batteries - p. 95 5. Chapter 5 State of charge estimation of energy storage battery based on long short-term memory neural network - p. 139 6. Chapter 6 Data-driven state of health estimation for energy storage battery clusters - p. 175 7. Chapter 7 Battery peak power estimation based on long- and short-term memory networks - p. 239 8. Chapter 8 Design and optimization of energy state assessment algorithms for energy storage batteries - p. 261 9. Chapter 9 Improved firefly optimized method for lithium-ion batteries of the co-estimation of state of charge and state of health - p. 297 10. Chapter 10 Joint estimation of state of charge and state of power for lithium-ion battery based on H-infinity filtering - p. 341 11. Chapter 11 Battery remaining useful life prediction based on multicore correlation vector machine - p. 381 12. Chapter 12 Intelligent balancing management of new power storage batteries - p. 433 13. References - p. 461 14. Index - p. 489
