Online Estimation and Error Analysis of both SOC and SOH of Lithium-ion Battery based on DEKF Method

Abstract

The state-of-charge (SOC) and state-of-health(SOH) are two critical indexes in battery management system (BMS) for electric vehicles(EVs). To achieve accurate estimation of SOC and SOH, this paper establishes a battery equivalent circuit model and uses Forgetting Factor Recursive Least Squares (FFRLS) to realize online identification of model parameters. And based on the relationship between the ohmic internal resistance and the SOH, a joint estimator using Double extended Kalman filter(DEKF) algorithm is proposed for the estimation of both SOC and SOH. Then, an error model is established to analyze the influence of the battery OCV-SOC curve, battery capacity and battery parameters on the estimation of the SOC and SOH. The experiment results show that the maximum estimation error of SOC and SOH is 1.08% and 1.52% respectively, which have verified that accurate and robust SOC and SOH estimation results can be obtained by the proposed method. Besides, the OCV-SOC curve has the greatest influence on the estimation error of SOC and SOH among the three kinds of factors mentioned above.