随着电动汽车的快速发展，动力电池荷电状态（SOC）的精确估计对整车能量管理、续驶里程预测及防止过充/过放至关重要。为了提高锂电池SOC估计精度，构建了一种基于分数阶理论的等效电路模型，并采用自适应遗忘因子递推最小二乘算法(adaptive forgetting factor recursive least square,AFFRLS)对模型参数进行辨识。针对传统无迹卡尔曼滤波(unscented Kalman filter,UKF)在动态工况下因单新息框架导致数据利用率低与抗扰性不足的问题，提出结合分数阶模型长记忆特性需求的多新息无迹卡尔曼滤波算法(fractional order multipleinformation unscented Kalman filter,FOMIUKF)。通过MATLAB搭建仿真模型，并与扩展卡尔曼滤波(extended Kalman filter,EKF)算法和无迹卡尔曼滤波算法进行比较，结果表明，基于FOMIUKF算法的SOC估计平均误差为0.78%，其精度比EKF算法提高了0.42%，比UKF算法提高了0.25%。

With the rapid development of electric vehicles, the accurate estimation for the state of charge (SOC) of power battery is crucial for vehicle energy management, range prediction and prevention of overcharge/overdischarge. In order to improve the estimation accuracy of lithium battery SOC, an equivalent circuit model was constructed based on the fractional order theory, and the model parameters were identified by using the adaptive forgetting factor recursive least square (AFFRLS) algorithm. In order to solve the problem of low data utilization and insufficient noise immunity caused by the single unscented Kalman filter (UKF) under dynamic conditions, a fractional order multiple-information unscented Kalman filter (FOMIUKF) algorithm was proposed by combining with the requirements of the long memory characteristics of fractional order model. The simulation model was built by MATLAB and compared with the extended Kalman filter (EKF) algorithm and UKF algorithm. The results showed that the average error of SOC estimation based on FOMIUKF algorithm was 0.78%, which was 0.42% higher than that of EKF algorithm and 0.25% higher than that of UKF algorithm.