Parameter identification and SOC estimation of lithium-ion battery based on AGCOA optimization

Considering the polarization effect of lithium-ion battery, this paper establishes a second-order RC model, and proposes an adaptive global optimal guided coyote optimal identification algorithm (AGCOA). It effectively solves the problems of traditional heuristic algorithms that are easy to fall into local optima and slow convergence. The battery testing equipment NEWARE BTS-4008 is used to charge and discharge the lithium-ion battery. The parameters identified by the AGCOA are used to predict the terminal voltage, and the extended Kalman filter algorithm (EKF) is used to estimate the battery SOC. The results show that the predicted voltage obtained by the identification is basically the same as the actual voltage, and the SOC estimation error is small, which verifies the efficiency and accuracy of the algorithm.