Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications

Abstract

For the vehicular operation, due to the voltage and power/energy requirements, the battery systems are usually composed of up to hundreds of cells connected in series or parallel. To accommodate the operation conditions, the battery management system (BMS) should estimate State of Charge (SOC) to facilitate safe and efficient utilization of the battery. The performance difference among the cells makes a pure pack SOC estimation hardly provide sufficient information, which at last affects the computation of available energy and power and the safety of the battery system. So for a reliable and accurate management, the BMS should “know” the SOC of each individual cell. Several possible solutions on this issue have been reported in the recent years. This paper studies a method to determine online all individual cell SOCs of a series-connected battery pack. This method, with an equivalent circuit based “averaged cell” model, estimates the battery pack’s average SOC first, and then incorporates the performance divergences between the “averaged cell” and each individual cell to generate the SOC estimations for all cells. This method is developed based on extended Kalman filter (EKF), and to reduce the computation cost, a dual time-scale implementation is designed. The method is validated using results obtained from the measurements of a Li-ion battery pack under three different tests, and analysis indicates the good performance of the algorithm.