Statistical cumulative error-based state of charge estimation for electric vehicle applications

Abstract

SOC is a crucial parameter in battery management systems (BMS), indicating the remaining amount of charge in a battery. Longer battery life and the removal of catastrophic battery damage are the results of accurate SOC assessment. Furthermore, it is crucial to have a dependable and precise estimation of SoC for an effective EV operation. Therefore, the lithium battery represents a characteristic nonlinear system, as well as the Extended Kalman Filter (EKF) algorithm proves to be a viable approach for SOC estimation. It is necessary to develop a new model called Modified EKF for SOC estimate based on EKF and KF in order to improve the stability and accuracy of the anticipated SOC. In this instance, the statistical cumulative error is utilized to determine the SOC using the Extended Kalman filter. Higher-order statistical characteristics including homogeneity, skewness, kurtosis, contrast, and entropy are taken into consideration in order to calculate the error involved in the estimation of SOC. Here, the suggested plan is to be simulated in MATLAB, and the temporal efficacy of the suggested approach is verified. The estimated Modified-EKF MAE is 0.2681%, the estimated SoC error RMSE is 0.34051%, and the estimated MSE is 0.11595%.