State-of-Charge Estimation for Lithium-ion Batteries Based on Fuzzy Information Granulation and Asymmetric Gaussian Membership Function

For power batteries used in the electric vehicle, accurate state-of-charge estimation is important. However, as one of the most commonly used estimation method, the least square support vector regression is hard to balance the accuracy and efficiency. To solve this problem, the fuzzy information granulation based on asymmetric Gaussian membership function is proposed to improve the utilization efficiency of the effective data and enhance the prediction accuracy of battery states. In addition, the performance of the proposed method is compared with that of other commonly used membership functions. In experiments, the dynamic stress test condition and the urban dynamometer driving schedule condition are used to verify the effectiveness. Compared with the most commonly used triangle membership function, the proposed method improves the accuracy of estimation by 6.47% and 2.18% under two current conditions, respectively.