State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter

Abstract

Lead-acid batteries are widely used in conventional internal-combustion-engined vehicles and in some electric vehicles. In order to improve the longevity, performance, reliability, density and economics of the batteries, a precise state-of-charge (SoC) estimation is required. The Kalman filter is one of the techniques used to determine the SoC. This filter assumes an a priori knowledge of the process and measurement noise covariance values. Estimation errors can be large or even divergent when incorrect a priori covariance values are utilized. These estimation errors can be reduced by using the adaptive Kalman filter, which adaptively modifies the covariance. In this study, an adaptive extended Kalman filter (AEKF) method is used to estimate the SoC. The AEKF can reduce the SoC estimation error, making it more reliable than using a priori process and measurement noise covariance values.