Battery State Estimation: Methods and models

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.

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.