基于小波降噪-曲线相似程度的锂离子电池内短路故障诊断方法

A lithium ion battery internal short circuit fault diagnosis method based on wavelet noise reduction and curve similarity

为实现对锂离子电池内短路故障的有效预警,提出一种基于小波降噪-曲线相似程度的锂离子电池内短路故障诊断方法。首先基于多分辨率的小波降噪方法,对锂离子电池充电电压曲线进行降噪;然后使用曲线相似程度确定故障报警阈值;最后通过模拟内短路实验获取早期故障电压数据,对数据进行多分辨率的小波降噪后,计算各循环充电电压曲线相似程度作为报警阈值。实验结果表明:所提出的基于小波降噪-曲线相似程度的锂离子电池内短路故障诊断方法能够有效实现故障预警,为锂离子电池内短路故障诊断提供了一种新的方法。

The internal short circuit fault of lithium ion battery is difficult to predict in the early stage,and the effective warning characteristics cannot be extracted by using traditional methods.In this paper,a fault diagnosis method based on wavelet de-noising and curve similarity in li-ion battery is proposed,which can effectively obtain the early fault characteristics and realize real-time online warning.Firstly,based on multi-resolution wavelet de-noising method,the voltage curve of lithium ion battery is de-noised.Then the curve similarity degree is used to determine the fault alarm threshold.Finally,the early fault voltage data is obtained through simulated internal short circuit experiment.After multi-resolution wavelet denoising is performed on the data,the similarity degree of each cycle charging voltage curve is calculated as the alarm threshold.The experimental results show that the proposed fault diagnosis method based on wavelet de-noising-curve similarity can effectively realize fault early warning and provide a new method for fault diagnosis of lithium ion batteries.