SOC estimation and simulation verification of lithium battery based on C0A-EKF

In order to make the State Of Charge(SOC) of lithium batteries in new energy vehicles more accurate, this paper uses the Coati Optimisation Algorithm(COA) to find the optimal noise variance value of the Extended Kalman Filter(EKF) to achieve a more accurate SOC estimation. After establishing the empirical model of the lithium battery, the Recursive Least Squares with Forgetting Factor (FFRLS) is used to identify its parameters, and the COA is used to establish the error correction model, and this is used to find the optimal noise covariance value of the gain matrix, which overcomes the problems of filter dispersion and large SOC estimation error due to real-time variation of battery parameters and uncertainty of filter noise is overcome. The results of the designed simulation experiments show that this optimisation algorithm can effectively solve the dispersion problem of filtering and improve the accuracy of SOC estimation, and has good convergence and robustness with high application value.