Battery Management System for Electric Vehicle and the Study of SOC Estimation

Abstract

The SOC (state of charge) of the Li-ion battery cells in a pack are different because of the property differences, which would lead to over-charging/over-discharging the battery pack and as a result the service life of the battery pack would be reduced. In this article, we designed a battery management system (BMS) for low voltage electric vehicle. The BMS adopted resistance shunt method to avoid over-charging the battery cells. Extended Kalman filter (EKF) was utilized for high precision estimation of SOC, which is very important for remaining the cells working within appropriate SOC and avoiding over-discharging the cells. Experiment result shows that comparing with the commonly used ampere-hour integration approach, EKF decreased estimation error from 15.48% to 7.27%. High precision SOC estimation algorithm and effective charge equalization method can maintain the battery cells working at a good situation and extend the service life of the battery pack, reducing the cost of use indirectly. This is meaningful for Li-ion battery's industrial application.