Recursive modeling and online identification of lithium-ion batteries for electric vehicle applications

Abstract

For safe and reliable operation of lithium-ion batteries in electric vehicles, the real-time monitoring of their internal states is important. The purpose of our study is to find an easily implementable, online identification method for lithium-ion batteries in electric vehicles. In this article, we propose an equivalent circuit model structure. Based on the model structure we derive the recursive mathematical description. The recursive extended least square algorithm is introduced to estimate the model parameters online. The accuracy and robustness are validated through experiments and simulations. Real-road driving cycle experiment shows that the proposed online identification method can achieve acceptable accuracy with the maximum error of less than 5.52%. In addition, it is proved that the proposed method can also be used to estimate the real-time SOH and SOC of the batteries.