Aiming at the problem of particle degradation in the state of charge (SOC) estimation of power batteries based on the particle filter (PF) algorithm, an algorithm combining adaptive iterative extended Kalman filter(AIEKF) and PF was proposed. Firstly, the AIEKF algorithm was used to update the state variables of the particles and optimize the allocation of particle weights to improve the diversity of particles. Then, the PF algorithm was combined to estimate the battery SOC. Finally, the simulation was carried out through the AMESim-MATLAB joint simulation platform. The simulation results show that under the DST working condition, the adaptive iterative extended Kalman particle filter (AIEKF-PF) algorithm reduces the average error by 0.493% and the root mean square error by 0.180% compared with the PF algorithm. Under the NEDC working condition, the AIEKF-PF algorithm reduces the average error by 0.811% and the root mean square error by 0.355% compared with the PF algorithm. The accuracy of the estimation results has been improved.