动力电池在复杂多变工况下，离线参数辨识无法实时反映电池动态特性导致参数辨识精度低，无迹卡尔曼滤波（untraceable Kalman filter，UKF）在估计电池荷电状态（State of charge，SOC）时对噪声处理十分有限，同时在处理协方差矩阵时出现非正定问题会导致算法波动和估计失效。基于双极化（dual polarization，DP）电路模型提出了遗忘因子递推最小二乘法（forgotten factor recursive least squares，FFRLS）和奇异值分解-自适应无迹卡尔曼滤波法（singular value decomposition-adaptive untraceable Kalman filter，SVD-AUKF）对电池SOC进行在线估计。仿真结果表明，在复杂工况下（美国联邦城市运行工况），与真实SOC值进行比较，SVD-AUKF进行模拟验证时平均绝对误差和均方根误差分别为0.528 6 %和0.544 7 %，在传统UKF算法基础上分别提高了57.96 %和63.3 %，进一步表明SVD-AUKF准确性和稳定性更高。

Aiming at the power battery under complex and variable working conditions, offline parameter identification cannot reflect the dynamic characteristics of the battery in real time, resulting in low parameter identification accuracy, and the untraceable Kalman filter (UKF) is very limited to deal with the noise when estimating the state of charge (SOC) of the battery, and at the same time, the non-positivity problem in the processing of the covariance matrix leads to algorithm fluctuations and estimation failures. In this paper, based on the dual polarization (DP) circuit model, the forgotten factor recursive least squares (FFRLS) and singular value decomposition-adaptive untraceable Kalman filter (SVD-AUKF) methods for online estimation of battery SOC are proposed. The simulation results show that the mean absolute error (MAE) and root mean square error (RMSE) of SVD-AUKF for simulation validation are 0.528 6 % and 0.544 7 %, respectively, when comparing with the real SOC values under the complex operating conditions (U.S.Federal Urban Operating Conditions (FUDS)), and the MAE and RMSE are improved on the basis of traditional UKF algorithm, respectively, by 57.96 % and 63.3 %, further indicating that SVD-AUKF is more accurate and stable.