锂离子电池荷电状态（state of charge,SOC）估算受温度影响显著。传统基于等效电路模型的方法需要在不同温度下进行离线开路电压（open circuit voltage,OCV）测试得到OCVSOC曲线，测试过程极为耗时，而基于大数据的方法需采集不同温度下的大量特征数据用于训练模型，两者均难以应用于在线估计。本研究提出一种适用于不同温度的SOC在线估算方法：采用增量式支持向量机（incremental support vector machine,ISVM）对电池端电压建模，结合小批量数据的在线采集实现模型参数动态更新，并融合自适应无迹卡尔曼滤波（adaptive unscented Kalman filter,AUKF）算法对SOC进行闭环估算。研究表明：在0 ℃，25 ℃和45 ℃三种典型温度下的联邦城市循环工况（federal urban driving schedule,FUDS）中，所提方法的SOC估算误差小于0.02%，平均绝对误差和均方根误差相较传统方法分别降低1.09个百分点和1.10个百分点，并对SOC初值具有良好的鲁棒性。

The state of charge (SOC) estimation for lithium-ion batteries is highly sensitive to temperature. Traditional methods based on equivalent circuit models require offline open circuit voltage (OCV) testing at different temperatures to obtain OCV-SOC curves, and the process is extremely time-consuming. Meanwhile, large data-based methods necessitate collecting large amounts of feature data at various temperatures for model training. Both approaches are difficult to apply for online estimation. A new online SOC estimation method across different temperatures was propesed. An incremental support vector machine (ISVM) models the terminal voltage, while online collection of mini-batch data enables dynamic model parameter updates. And an adaptive unscented Kalman filter (AUKF) ensures closed-loop estimation. Tests under federal urban driving schedule (FUDS) at 0 ℃, 25 ℃, and 45 ℃ show that the method achieves SOC estimation errors within 0.02%.Compared to conventional methods, the mean absolute error and root mean square error reduce by 1.09 percentage points and 1.10 percentage points, respectively. The method also exhibits strong robustness to initial SOC values.