基于小波分解和Hilbert包络谱分析的电池故障诊断方法

Battery fault diagnosis method based on wavelet decomposition and Hilbert envelope spectrum analysis

目前,电动汽车安全事故频繁发生,严重影响驾驶人员的生命和财产安全,因此在电池严重故障发生之前如果能够进行有效的诊断和预警,将确保电动汽车安全运行。提出一种基于小波分解和希尔伯特(Hilbert)包络谱分析的电池故障诊断方法。首先,利用小波分解得到合适的分解信号;其次,选取合适的细节分解信号进行希尔伯特变换得到包络谱;然后提取出有效故障特征,并利用离群点检测算法进行故障电池检测。实车试验结果表明,该方法能有效提取故障特征,并提前实现电池故障诊断。

At present,electric vehicle safety accidents occur frequently,which seriously affect the life and property safety of drivers.Therefore,if effective diagnosis and early warning can be carried out before the occurrence of serious battery failure,the safe operation of electric vehicles will be ensured.A battery fault diagnosis method based on wavelet decomposition and Hilbert envelope spectrum analysis is proposed.Firstly,the proper decomposition signal is obtained by wavelet decomposition.Secondly,the envelope spectrum is obtained by Hilbert transform of appropriate detail decomposition signals.Then the effective fault features are extracted and the outlier detection algorithm is used to detect the faulty battery.The experimental results show that this method can extract fault features effectively and realize battery fault diagnosis in advance.