改进粒子滤波算法对电动汽车电池SOC的估计

An Improved Particle Filter Algorithm for SOC Estimation of Electric Vehicle Battery

引入SIR粒子滤波算法用于估算电动汽车电池的荷电状态(State of charge,SOC),利用系统状态连续近似分布进行采样的正则化滤波算法解决了SIR粒子滤波算法多样性匮乏问题。结合安时法构建电动汽车电池的状态空间模型,进而对电池模型进行参数辨别,结合SIR粒子滤波算法和改进后的粒子滤波算法在MATLAB中进行实验仿真。仿真结果显示,随着时间的增加,SIR粒子滤波算法估算电池SOC误差会变大,改进后的粒子滤波算法估算电池SOC一直逼近真实值,比SIR粒子滤波算法精度高、适应性更好,为电动汽车电池SOC的估算提供了新思路。

In order to solve the problem of lacking SIR particle filter algorithm diversity, the SIR particle filter algorithm is improved to estimate electric vehicle battery state of charge （SOC）, with system state continuous approximate distribution sampling regularization filtering algorithm. By the ampere hour method to build the state space model of the battery and identify the battery model parameter, the simulation experiment is finished combined with the particle filter algorithm and improved particle filter algorithm in MATLAB. Simulation results show that, the SIR particle filter algorithm estimation errors of SOC becomes larger with the time increasing, the improved particle filtering algorithm to estimate the battery state of charge （SOC） has been close to the true value. Compared with the SIR particle filter, the improved particle filtering algorithm is of high accuracy and better adaptability than the SIR particle filter algorithm, providing a new idea for estimating SOC of batteries used in electric vehicles.