Power battery parameter online identification for electric vehicle using a decoupling multiple forgetting factors recursive least squares method

Abstract

Li-ion batteries are widely used in electric vehicles (EVs). However, the accuracy of online SOC estimation is still challenging due to the time-varying parameters in batteries. This paper proposes a decoupling multiple forgetting factors recursive least squares method (DMFFRLS) for EV battery parameter identification. The errors caused by the different parameters are separated and each parameter is tracked independently taking into account the different physical characteristics of the battery parameters. The Thevenin equivalent circuit model (ECM) is employed considering the complexity of battery management system (BMS) on the basis of comparative analysis of several common battery ECMs. In addition, decoupling multiple forgetting factors are used to update the covariance due to different degrees of error of each parameter in the identification process. Numerous experiments are employed to verify the proposed DMFFRLS method. The parameters for commonly used LiFePO 4 (LFP), Li (NiCoMn)O 2 (NCM) battery cells and battery packs are identified based on the proposed DMFFRLS method and three conventional methods. The experimental results show that the error of the DMFFRLS method is less than 15 mV, which is significantly lower than the conventional methods. The proposed DMFFRLS shows good performance for parameter identification on different kind of batteries, and provides a basis for state of charge (SOC) estimation and BMS design of EVs.