为了提高锂电池荷电状态 （SOC）估计的精度，提出了一种基于动态遗忘因子递推最小二乘法和改进粒子滤波算法相结合的锂电池SOC估计方法。针对固定遗忘因子递推最小二乘法在电池参数辨识中难以同时保持快速收敛和稳定性的问题，引入动态遗传因子，以模型辨识值和实际值的残差为变量构建修正公式，实现遗忘因子动态调整。为了改善粒子滤波（PF）的粒子多样性丧失问题，采用白鹭群优化算法（ESOA）对粒子滤波算法进行优化。仿真结果表明，基于动态遗忘因子递推最小二乘法和改进粒子滤波算法的锂电池SOC估计误差始终保持在0.3%以内，平均绝对误差和标准差为0.15%和0.17%，与其他算法相比具有更好的精度和稳定性。

In order to improve the accuracy of SOC estimation for lithium battery, a lithium battery SOC estimation method was proposed based on the combination of dynamic forgetting factor recursive least squares and improved particle filtering algorithm. The fixed forgetting factor recursive least squares method was difficult to maintain the fast convergence and recognition accuracy at the same time in battery parameter identification, a dynamic genetic factor was hence introduced and the residual difference between the identified and actual values of model was used as the variable to construct a correction formula to achieve the dynamic adjustment of forgetting factor. In order to improve the problem of particle diversity loss in particle filter (PF), the egret swarm optimization algorithm (ESOA) was used to optimize the particle filtering algorithm. The simulation results show that the estimation error of lithium battery SOC always remains within 0.3% after using the dynamic forgetting factor recursive least squares method and the improved particle filtering algorithm, with the mean absolute error and standard deviation of 0.15% and 0.17%. Compared with other algorithms, the new algorithm has better accuracy and stability.