State-of-charge estimation for lithium-ion batteries based on dual extended Kalman filter

Abstract

The strategy of estimating SOC by model is highly dependent on the accuracy of model. An SOC estimation method based on a dual extended Kalman fliter (DEKF) is proposed. One of the dual filters is employed to estimate the battery SOC, and the other is used to online identify the model parameters. The SOC estimation results by DEKF are compared with those by single EKF under the US06 Highway Driving Schedule test and Dynamic Stress Test (DST). The comparison results show that DEKF has higher SOC estimation and voltage prediction accuracy. Under the US06 and DST tests, the SOC mean absolute error (MAE) decreases from 2.57% and 3.00% to 1.18% and 1.56%, and the MAE of voltage prediction decreases from 54.6 mV and 38.3 mV to 43.9 mV and 29.1 mV, respectively. The SOC estimation and voltage prediction results demonstrate the effectiveness and accuracy of the proposed method.