A method to identify lithium battery parameters and estimate SOC based on different temperatures and driving conditions

Abstract

State of charge (SOC) plays a significant role in the battery management system (BMS), since it can contribute to the establishment of energy management for electric vehicles. Unfortunately, SOC cannot be measured directly. Various single Kalman filters, however, are capable of estimating SOC. Under different working conditions, the SOC estimation error will increase because the battery parameters cannot be estimated in real time. In order to obtain a more accurate and applicable SOC estimation than that of a single Kalman filter under different driving conditions and temperatures, a second-order resistor capacitor (RC) equivalent circuit model (ECM) of a battery was established in this paper. Thereafter, a dual filter, i.e., an unscented Kalman filter–extended Kalman filter (UKF–EKF) was developed. With the EKF updating battery parameters and the UKF estimating the SOC, UKF–EKF has the ability to identify parameters and predict the SOC of the battery simultaneously. The dual filter was verified under two different driving conditions and three different temperatures, and the results showed that the dual filter has an improvement on SOC estimation.