State of charge estimation based on extened Kalman filter algorithm for Lithium-Ion battery

Abstract

Estimation of the state of charge (SOC) is a critical parameter for the control of propulsion systems in plug-in hybrid electric vehicles (PHEV) and the electric vehicles (EVs). This paper proposes the SOC estimator of a Lithium-Ion battery using the adaptive extended Kalman filter (EKF). This method uses an optimization algorithm to update the EKF model parameters during a charge period. Accurate knowledge of the nonlinear relationship between the open circuit voltage (OCV) and the SOC is required for adaptive SOC tracking during battery usage. EKF is employed to estimate the SOC by considering it as one of the states of the battery system. The dynamic model structure adopted is based on an equivalent circuit model whose parameters are scheduled on the SOC, temperature, and current direction. The validity of the procedure is demonstrated experimentally for an A123 systems' APR18650m1 LiFePO4 battery.