The purpose of this paper is to improve the monitoring accuracy of state of charge (SOC) for batteries. Based on the equivalent circuit model of lithiumion batteries

the Elephant herding optimization (EHO) is employed to enhance the identification of model parameters through Kalman filtering (KF). The seagull optimization algorithm (SOA) is utilized to reduce the impact of initial noise values on the extended Kalman filter (EKF) algorithm

while employing an out-of-range processing strategy to avoid the reduction of population diversity. The modified seagull optimization algorithm (MSOA) is applied to optimize the EKF and improve the SOC estimation method for vehicle batteries

which is validated using DST and FDUS dynamic operating current data. The results demonstrate that the improved SOC estimation algorithm yields an error rate lower than 0.97%. Furthermore

the estimated error rates of root mean squared error(RMSE) and mean absolute error(MAE) are both lower than those of the EKF algorithms

indicating that the MSOA-optimized EKF algorithm offers superior estimation accuracy and stability.