Battery State Estimation: Methods and Models
Language: English
Published by Institution Of Engineering & Technology Jan 2022, 2022
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 176.30
Quantity: 2 available
Add to basketItem description from seller
Neuware - Batteries are of vital importance for storing intermittent renewable energy for stationary and mobile applications. In order to charge the battery and maintain its capacity, the states of the battery - such as the current charge, safety and health, but also quantities that cannot be measured directly - need to be known to the battery management system. State estimation estimates the electrical state of a system by eliminating inaccuracies and errors from measurement data. Numerous methods and techniques are used for lithium-ion and other batteries. The various battery models seek to simplify the circuitry used in the battery management system. This concise work captures the methods and techniques for state estimation needed to keep batteries reliable. The book focuses particularly on mechanisms, parameters and influencing factors. Chapters convey equivalent modelling and several Kalman filtering techniques, including adaptive extended Kalman filtering for multiple battery state estimation, dual extended Kalman filtering prediction for complex working conditions, and particle filtering of safety estimation considering the capacity fading effect. This book is necessary reading for researchers in battery research and development, including battery management systems and related power electronics, for battery manufacturers, and for advanced students in power electronics.…
Seller Inventory # 9781839535291
- Title
- Battery State Estimation: Methods and Models
- Author
- Shunli Wang
- Publisher
- Institution Of Engineering & Technology Jan 2022
- Publication year
- 2022
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 1839535296
- ISBN 13
- 9781839535291
- Item weight
- 635 grams
- Dimensions
- 239x160x20 mm
Batteries are of vital importance for storing intermittent renewable energy for stationary and mobile applications. In order to charge the battery and maintain its capacity, the states of the battery - such as the current charge, safety and health, but also quantities that cannot be measured directly - need to be known to the battery management system. State estimation estimates the electrical state of a system by eliminating inaccuracies and errors from measurement data. Numerous methods and techniques are used for lithium-ion and other batteries. The various battery models seek to simplify the circuitry used in the battery management system.
This concise work captures the methods and techniques for state estimation needed to keep batteries reliable. The book focuses particularly on mechanisms, parameters and influencing factors. Chapters convey equivalent modelling and several Kalman filtering techniques, including adaptive extended Kalman filtering for multiple battery state estimation, dual extended Kalman filtering prediction for complex working conditions, and particle filtering of safety estimation considering the capacity fading effect.
This book is necessary reading for researchers in battery research and development, including battery management systems and related power electronics, for battery manufacturers, and for advanced students in power electronics.
"Synopsis" may belong to another edition of this title.
About the Author
Shunli Wang is a professor at Southwest University of Science and Technology, China, where he heads the New Energy Measurement and Control Research Team. His research focuses on modeling and state estimation research for batteries and multiple generation battery systems. He holds 30 patents, has published more than 100 papers, and won several awards.
"About the title" may belong to another edition of this title.
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