An EKF Algorithm for Lithium-ion Battery SOC Estimation Based on an Equivalent Circuit Model

Abstract

This paper investigates the battery state of charge (SOC) estimation problem in the extended Kalman filter (EKF) method to enhance the estimation accuracy of SOC. For the lithium-ion battery, the second-order RC equivalent circuit model is constructed where the functional relationship among the ohmic internal resistance, open-circuit voltage, polarization capacitance and polarization internal resistance with SOC is obtained. By applying the offline identification method, parameters of this model are obtained, meanwhile, the model accuracy is analyzed. Subsequently, the SOC value of the lithium-ion battery is estimated by the EKF technique which has advantages over the ampere-hour method. The results show that the maximum errors estimated by the EKF method for the second-order RC model under HPPC and BBDST conditions are 4.7% and 2.4% respectively. It is obvious that in the presence of SOC initial error and detection noise, EKF technology can quickly and accurately estimate the SOC value.