Estimation of lithium battery state of charge based on N-order EKF

Abstract To address the issue of the Extended Kalman Filter (EKF) algorithm ignoring higher-order terms during nonlinear transformations near the estimated state point, resulting in errors in State of Charge (SOC) estimation, the author established a second-order RC equivalent circuit model. Additionally, an impulse characteristic experiment was conducted to identify the internal parameters of the model offline. The SOC of lithium batteries can be tracked and estimated using the EKF algorithm, which has been enhanced by integrating the high-order expansion of the Taylor formula. Matlab simulation results confirm that the N-order EKF algorithm has better robustness and convergence than lower-order EKF algorithms, with a Mean Absolute Error (MAE) of less than 0.5% in dynamic and static battery estimation.