An extended Kalman filter based data-driven method for state of charge estimation of Li-ion batteries

Abstract

Accurate state-of-charge (SOC) estimation, which can effectively prevent battery overcharge and over-discharge, provide accurate driving range and extend battery life, is challenging due to complicated battery dynamics and ever-changing ambient conditions. In this paper, an extended Kalman filter (EKF) based data-driven method for SOC estimation of Li-ion Batteries is proposed. First, the model characteristic parameters are dynamically tracked through the recursive least squares method. Then the filtered output result of the EKF algorithm reflecting the battery's dynamic characteristics is used as the training data of the extreme gradient boosting (XGBoost) model. Benefit from the XGBoost's excellent machine learning and predictive capabilities, which can realize the battery's SOC high-precision prediction based on the EKF algorithm. The simulation results show that the proposed XGBoost algorithm, especially in the low SOC range, has good convergence and robustness, compared with the EKF algorithm and the library for support vector machines (LIBSVM) algorithm. The algorithm is validated under different driving conditions and achieves accurate SOC estimation with a maximum absolute error of less than 2%. It can be easily concluded that the proposed method can achieve accurate and stable SOC estimation, effectively avoiding the open-loop risk of data-driven algorithms.