State-of-charge Estimation of Lithium-ion Polymer Battery Based on Sliding Mode Observer

In order to estimate the state-of-charge (SOC) for lithium-ion polymer battery of electric vehicle, an improved Thevenin battery model is achieved, and the model parameters are identified online by adopting the extended Kalman filter (EKF) algorithm. By introducing Luenberger-type feedback terms, a sliding mode observer for estimating SOC is proposed, and a sufficient condition is derived to guarantee the convergence of the observer. Finally, the proposed method is verified and evaluated by experiments. Additionally, it is compared with the EKF method. The results show that, SOC estimation with the sliding mode observer has higher accuracy than EKF method, and gives the maximum error of 1.3059% with variance of 0.00002. This method is proved to have good convergence, and can efficiently solve the problem of inaccurate initial-value estimation.