A Kalman Filter Based Battery State of Charge Estimation MATLAB Function

This paper proposes a Kalman filter based state-of-charge (SOC) estimation MATLAB function using a second-order RC equivalent circuit model (ECM). The function requires the SOC-OCV (open circuit voltage) curve, internal resistance, and second-order RC ECM battery parameters. Users have an option to use an extended Kalman filter (EKF) or adaptive extended Kalman filter (AEKF) algorithms as well as temperature dependent battery data. An example of the function is illustrated using the LA92 driving cycle of a Turnigy battery performed at multiple temperature ranging from −10°C to 40°C.