Accurate state of charge (SOC) estimation is an important evaluation index for battery management system. However, the SOC estimation accuracy is influenced by many factors, in which aging is one of the most important factors. Therefore, real-time parameter identification is necessary for accurate SOC estimation. In this paper, we proposed a dual adaptive extended Kalman filter algorithm for the SOC and parameter co-estimation of liquid metal battery, which is used in the stationary energy storage. Simulation and experiment results prove its superior performance in accuracy compared with conventional methods.
Keywords state of charge, battery management system, parameter identification, dual adaptive extended Kalman filter, stationary energy storage