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.

• Chapter 1: Introduction
• Chapter 2: Mechanism and influencing factors of lithium-ion batteries
• Chapter 3: Equivalent modeling, improvement, and state-space description
• Chapter 4: Extended Kalman filtering and its extension
• Chapter 5: Adaptive extended Kalman filtering for multiple battery state estimation
• Chapter 6: Dual extended Kalman filtering prediction for complex working conditions
• Chapter 7: Unscented particle filtering of safety estimation considering capacity fading effect