融合改进方差和样本熵的动力电池早期故障诊断

Early Fault Diagnosis of Power Battery Based on Improved Variance and Sample Entropy

针对汽车动力电池早期微小故障难以检测的问题,论文基于某车实际运行电压数据,提出一种基于修正方差和样本熵相结合的动力电池故障诊断方法。首先获取某辆车动力电池系统充电片段电压数据,通过修正方差算法计算每个单体的修正方差,根据单体方差的大小对早期故障进行初步判定;同时结合单体电压序列样本熵值的大小变化情况实现汽车动力电池早期故障的精准诊断。结果表明,所提方法能够有效减弱电池单体电压不一致性的干扰,尽早挖掘出电池单体中潜藏的早期微小故障,同时样本熵的融合进一步提高了早期故障诊断的准确性。

In order to solve the problem that it is difficult to detect the early minor faults of automotive power battery,this paper proposes a fault diagnosis method based on the combination of modified variance and sample entropy based on the actual operating voltage data of a vehicle.Firstly,the voltage data of the charging segment of a vehicle's power battery system is obtained to calculate the modified variance of each individual through the modified variance algorithm,and preliminarily determine the early fault according to the size of the individual variance.At the same time,combined with the change of entropy value of individual voltage sequence samples,accurate early fault diagnosis of automobile power battery is realized.The results show that the method proposed in this paper can effectively reduce the interference of battery cell voltage inconsistency,and dig out the early micro faults hidden in the battery cell as early as possible.At the same time,the fusion of sample entropy further improves the accuracy of early fault diagnosis.