State of Charge Estimation Based on Cumulative Error Based-Extended Kalman Filter for Electric Vehicle Applications

The Battery Management System (BMS) is crucial for the effective operation of lithium-ion battery (LIB) systems, particularly in estimating State of Charge (SOC). Given that LIBs exhibit nonlinear behavior, the Extended Kalman Filter (EKF) algorithm proves to be an effective method for estimating SOC. However, neglecting higher-order components can lead to inaccuracies in SOC estimation and potential divergence in the estimation process. To enhance the reliability of SOC estimates, an adaptive EKF is utilized, combining elements of both the Kalman Filter (KF) and EKF. This work develops a battery management system that includes a model-driven SOC estimation approach. The proposed approach employs a cumulative error-based EKF for SOC estimation. This cumulative error is derived from a novel mathematical model that utilizes historical SOC data from the battery, thereby improving estimation accuracy. The proposed method achieved minimum Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) of 0.3352, 0.1697, and 0.412, respectively.