State of charge and state of health estimation of lithium battery using dual Kalman filter method

Abstract

In a Battery Management System (BMS), the determinations of state of charge (SOC) and state of health (SOH) of Lithium battery in hybrid electric vehicle (HEV) need to be accurate and reliable. Charging and discharging processes occurred in Lithium Battery used in HEV produce different SOC value over time, and an accurate prediction method is needed to estimate the SOC current condition. In the long run, both processes can reduce the SOH value of the lithium battery. One of the methods to estimate SOC and SOH values is Coulomb Counting which calculates the coulomb rate flowing out of the Lithium battery over time, but the calculation cannot be done in real time. In this paper, the dual Kalman filter method to predict Lithium Battery SOC and SOH values in real time are utilized. The experimental results show that the estimation of SOC and SOH by using the dual Kalman filter provides almost the same value with that by using the Coulomb counting method.