State-of-charge estimation of electric vehicles using Kalman filter for a harsh environment; a heuristic method for current measurement error calibration

摘要

In this paper, it has been studied how to estimate state-of-charge (SOC) with accuracy and robustness in a harsh environment. Several methods have been used for that purpose. More specifically, in the process of estimation of open circuit voltage (OCV), least mean square (LMS) is used to calculate parameters of ECM, and Kalman filter (KF) is proposed to estimate SOC using both current integral and SOC obtained from OCV. The actual current pattern is used to verify the method in a harsh environment. This method can obtain the result that errors in SOC estimation, occurred in the whole process, is relatively smaller than other methods. Accurate SOC enables more efficient battery management. As a result, the utilisation of a battery can be increased using the method this paper proposes.