Online Parameters and State of Charge Co-estimation of Lithium-Ion Battery in Varying Temperature Using Joint Extended Kalman Filter

Abstract

This paper compares the mode based lithium-ion battery state of charge (SOC) estimations using offline and online parameters under varying temperature. An innovative offline identification method based on genetic algorithm (GA) is used for off-line identification of battery model parameters. The common extended Kalman filter (EKF) and the joint extended Kalman filter (JEKF) are implemented as the algorithms to implement SOC estimation with offline and online parameters. The SOC estimations by JEKF using online parameters and by EKF using offline parameters from mismatched temperature are compared. The results are as follows. When battery temperature is inaccurate, the inaccurate temperature can result in inaccurate offline parameters parameters, which will further increase the SOC estimation errors by EKF using offline parameters. In contrast, SOC estimation accuracy by JEKF are still accurate when no temperature information is provided, because the parameters are online updated by JEKF.