Battery state of charge (SOC) estimation is helpful to alleviate the mileage anxiety in the process of driving. Aiming at the problem of particle degradation in the estimation of SOC by particle filter, this paper proposes to apply the Gaussian particle swarm optimization particle filter ( GPSO-PF). Compared to estimation of SOC by particle filter, GPSO-PF combines particle swarm optimization algorithm and particle filter to estimate SOC. GPSO-PF solve the problem of particle dilution and improve the estimation accuracy of SOC by continuously optimizing the position of particles in the iteration. As SOC estimation is easily affected by temperature, an equivalent circuit model based on temperature is established and applied to the proposed SOC estimation algorithm. Two LiFePO4 batteries of the same type are selected and the GPSO-PF algorithm is used to estimate the SOC value under different working conditions. The maximum estimation error of SOC is less than 0. 72%. By comparison, GPSO-PF algorithm combined with equivalent circuit model based on temperature can effectively improve the estimation accuracy of SOC.