Research on Online Identification of Lithium-ion Battery Equivalent Circuit Model Parameters
Yahui Wu, Haishan Chen, Liangqiang Cao, Jinjin Duan, Xu Chen, Jiyao Zhai
Abstract
The selection of the lithium-ion battery equivalent model and the identification of battery state parameters are the focus of its research field. The battery parameter identification includes two methods, offline and online. Offline recognition is not only a time-consuming process, but also produces insufficiently accurate results. In order to achieve accurate estimation of the power battery state and accurate identification of parameters, the accuracy of the battery model needs to be improved. In this paper, a ternary lithium-ion battery is used as the research object, and a second-order RC equivalent circuit model is established. First, the HPPC cycle test experiment is used to identify the battery parameters offline, and then the forgetting factor recursive least square method is used to calculate the equivalent circuit model. Conducted online parameter identification, and built a simulation model in the MATLAB/Simulink software platform to verify the results of online identification, compared with offline identification methods, and analyzed the advantages and disadvantages of online identification. The research results show that the model parameters obtained by the genetic factor recursive least square method are more accurate than offline identification, and can reflect the internal state parameters of the battery in real time. It is a good online identification method, which lays the foundation for practical application.