A battery model with adaptive parameters based on equivalent circuit for state of charge estimation

A battery model with adaptive parameters based on equivalent circuit is proposed to solve the problems that it is complex to identify the parameters of a battery model online and errors of the battery model will dramatically enlarge while the parameters of the battery model varies. An observer with adaptive parameters for batteries is designed and is proved to be stable. Parameters are estimated and filtered online by the observer and a moving average filter, respectively. The battery model is periodically updated by previously estimated parameters. Then, the extended Kalman filtering algorithm is adopted to estimate the state of charge (SOC) of the battery. An experimental platform is constructed, and the urban dynamometer driving schedule (UDDS) driving cycle is used to test the algorithm. The results show that the error of SOC estimation based on the proposed model and the dynamic Kalman filter is less than 3%. It can be concluded that the algorithm is accurate and has great value to monitor power batteries in changeful environment.