考虑测量野值的锂电池异常数据检测与SOC估计算法

Abnormal Data Detection and SOC Estimation Algorithm for Lithium Battery Considering Measured Outliers

针对锂电池状态估计中传感器测量出现野值的问题,文中设计了卡方检测器和相应的滤波算法,消除了野值对锂电池荷电状态估计的影响。通过二阶RC等效电路对电池动力学模型进行描述,并采用卡尔曼滤波的方法离线辨识电池模型的参数。在考虑了传感器数据存在野值的情况下,采用卡方检测器对野值进行实时检测。当检测到野值发生时,根据零阶保持的思想提出一种只依赖于模型的SOC估计算法,有效地抵抗了测量野值。在FUDS工况下,通过实验仿真发现,文中设计的野值检测器和改进的SOC估计算法能够精确检测到野值的发生,并可保证SOC的估计误差在2%以内,体现了良好的估计性能。

In view of the problem of outliers in sensor measurement in lithium battery,a chi-square detector and corresponding filtering algorithm are designed to eliminate the influence of outliers on lithium battery state of charge estimation in this study.The second-order RC equivalent circuit is selected to describe the battery dynamic model,and the parameters of the battery model are identified by Kalman filter in an off-line way.Considering the existence of outliers in sensor data,the chi-square detector is used to detect the outliers in real-time.When outliers are detected,an improved SOC estimation algorithm that only depends on the model is proposed according to the idea of zero-order preservation,which can resist the measured outliers well.Under FUDS condition,the experimental simulation shows that the designed outlier detector and the improved SOC estimation algorithm can accurately detect the occurrence of outliers,and the estimation error of SOC is guaranteed within 2%,reflecting good estimation performance.