Battery State of Charge Estimation Using Adaptive Extended Kalman Filter for Electric Vehicle application

Abstract

To build up a proficient battery management system, it is required to accurately estimate the state of charge (SOC) of the electric vehicle (EV) battery. Generally, the accuracy of the conventional extended Kalman Filter (CEKF) algorithm is exceptionally affected by the method used to update the noise covariance matrices under running conditions. In this work, the new adaptive extended Kalman filter (AEKF) algorithm is designed for the SOC estimation. Methods such as forgetting factor method and moving window are used for estimation of measurement noise and sensor noise covariance matrix respectively. Pulse discharge and customized dynamic stress tests are conducted to check the robustness of the proposed algorithm. Experimental results indicated that proposed AEKF has superior performance than CEKF under dynamic load conditions.