Novel strategy based on improved Kalman filter algorithm for state of health evaluation of hybrid electric vehicles Li-ion batteries during short- and longer term operating conditions

Abstract

To solve the problems in estimating the state of health (SOH) of Li-ion batteries due to real-time estimation difficulty and low precision under various operating conditions, the variations of the SOH caused by increases of the internal resistance have been analyzed. Based on the second-order RC equivalent circuit model, the short-term effect of the state of charge (SOC) on the internal resistance was considered, which was set under the discharge condition. In addition, the variation of the internal resistance was analyzed in two intervals of 0–1 s and 1–10 s. The extended Kalman filter (EKF) algorithm was improved to present a novel improved Kalman filter (IKF) algorithm to accurately predict the long-term internal resistance under different operating conditions. A computational formula based on the internal-resistance increasing was established and the SOH was estimated. The error of the calculated result when compared with the forgetting factor least square method based on the internal-resistance increasing was controlled to within 4.0% under the HPPC condition, 3.0% under the BBDST condition, and 6.0% under the DST condition. The proposed algorithm has good convergence, helps improve the SOH estimation, and encourages the application of Li-ion batteries.