锂离子电池SOC的无迹卡尔曼信息融合估计算法

摘要

Battery state of charge ( SOC) is one of the important performance indexes for the lithium -ion batter-y, and its accuracy can directly determine the battery service life .Since multi-sensor fusion estimation can po-tentially improve estimation accuracy , it introduces the unscented information fusion filtering method to design SOC estimator .Comparing with the existed SOC estimation method , unscented information fusion filtering algo-rithm for the SOC estimation has stronger fault tolerance and higher estimation accuracy .Finally, the simulation results verify the effectiveness of the proposed SOC estimation algorithm .%电池荷电状态(SOC)是锂离子电池的重要性能指标,其估计精度直接影响电池的使用寿命.为了提高SOC估计性能,引入多传感器数据融合策略,利用无迹信息融合滤波器在处理非线性系统上的优势设计了SOC估计算法,与无迹卡尔曼滤波算法相比,无迹信息融合估计算法在SOC估计上具有更好的容错性和估计精度.仿真实验验证了该算法在SOC估计上的有效性.