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.
