Improved parameter identification and state-of-charge estimation for lithium-ion battery with fixed memory recursive least squares and sigma-point Kalman filter

Abstract

Accurate estimation of state-of-charge (SOC) of lithium-ion batteries (LIBs) is one of the important tasks of the on-board battery management system (BMS) to ensure the safe, efficient and reliable operation of electric vehicle power battery packs. In order for the BMS to monitor and predict battery behavior, an accurate battery model is needed to establish the relationship between the measurable external characteristic quantities (e.g., voltage, current and temperature) and the battery state. In this paper, a 2-resistor-capacitor (RC) network equivalent circuit model (ECM) is adopted and the hysteresis effect is considered to improve its accuracy. Thereafter, a novel online joint SOC estimation method combining the fixed memory recursive least squares (FMRLS) method and sigma-point Kalman filter (SPKF) algorithm is proposed to dynamically identify the model parameters and estimate the battery SOC. A dataset consisting of data from a dynamic stress test (DST) and a federal urban driving schedule (FUDS) test is then used to verify the proposed method. The results show that the joint SOC estimation method yields a significantly higher SOC estimation precision than the single SPKF estimation method on the basis of accurately tracking the dynamic changes of model parameters, and the addition of the hysteresis to the ECM also has a significant effect on improving the SOC estimation precision.