基于改进无迹卡尔曼滤波的锂电池SOC在线估计

针对无迹卡尔曼滤波算法对电池模型敏感并且容易受到不确定噪声干扰的问题，提出了改进的无迹卡尔曼滤波算法（improved unscented Kalman filter，IUKF），提高电池荷电状态（State of charge，SOC）估计精度和鲁棒性能。首先，对锂离子电池进行建模并完成参数离线辨识。紧接着，对模型参数进行敏感性分析，研究不同参数对SOC估计效果的影响程度，为模型参数自适应对象的选取提供依据。随后，研究了包含模型自适应算法和噪声自适应算法在内的IUKF算法实现过程。最后，通过物理实验对比分析了IUKF与其它算法的实际估计效果，实验结果表明，该方法估计误差小于1.79%，鲁棒性能良好。

Online Estimation of SOC for Li-ion Battery Based on An Improved Unscented Kalman Filters Approach

An improved unscented Kalman filter (IUKF) approach was proposed to enhance the accuracy and robustness of state of charge (SOC) online estimation, aimed at improving the drawbacks of unscented Kalman filter (UKF) which needs an accurate model and a priori noise statistics. Firstly, Li-ion battery modeling and offline parameters identification were realized. Secondly, sensitivity analysis experiment of the cell’s electrical model was designed to verify which model parameter has the most important influence on the SOC estimation accuracy, and provide the appropriate parameter for the model adaptive algorithm. Thirdly, IUKF approach composed of model adaptive algorithm and noise adaptive algorithm was introduced. Finally, this method was verified through physical experiment. The experimental results revealed that the proposed approach’s estimation error is less than 1.79% with acceptable robustness.