基于粒子群优化粒子滤波算法的 SOC 估算研究

准确的获得电池的荷电状态(SOC)有助于缓解汽车行驶过程中的里程焦虑。针对粒子滤波估算SOC中存在的粒子退化的问题，将粒子群算法与粒子滤波融合的改进粒子滤波算法(GPSO-PF)算法应用于SOC的估计。在迭代中不断优化粒子所处位置，从而解决了粒子贫化的问题，提高了SOC的估算精度。同时，针对SOC估算容易受到温度的影响，建立基于温度的等效电路模型，并将其应用于提出的SOC估算算法中。选取两节相同型号的磷酸铁锂电池，分别在不同工况下利用GPSO-PF算法估算SOC值，SOC的最大估算误差均低于0.72%。通过对比，与基于温度等效电路模型相结合后，GPSO-PF算法能够有效提高SOC的估算精度。

Research on SOC estimation based on particle swarm algorithm and particle filter algorithm

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.