基于小波分解和GDI的动力电池故障诊断  

Fault diagnosis for power battery based on wavelet decomposition and GDI

作　　者：刘光军 张恒[1] LIU Guang-jun;ZHANG Heng(Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy,Hubei University of Technology,Wuhan,Hubei,430068,China)

机构地区：[1]湖北工业大学太阳能高效利用及储能运行控制湖北省重点实验室,湖北武汉430068

出　　处：《电池》2023年第2期165-168,共4页Battery Bimonthly

基　　金：国家自然科学基金(52177212);湖北省教育厅科学研究计划(T2021005)

摘　　要：在动力电池组故障早期准确地定位故障单体电池,能预防安全事故。提出基于小波分解和广义无量纲指标(GDI)的动力电池故障诊断方法。使用小波分解,从电压数据中提取稳定的趋势分量;使用自定义的GDI提取故障信息;使用微分法处理故障信息,排除电池组不一致的情况,并使用3-σ原则作为故障触发阈值。基于电动汽车实际运行数据的实验结果表明,所提方法较信息熵法准确性更高,且具有较强的鲁棒性,在故障早期能够准确地定位故障单体电池,并降低电池组不一致故障的误报率。Accurately locating the fault cell in the early stage of power batteries fault could prevent safety accidents.A power battery fault diagnosis method based on wavelet decomposition and generalized dimensionless indicator(GDI)was proposed.Wavelet decomposition was used to extract stable trend components from voltage data,the fault information was extracted using the user-defined GDI.The differential method was used to process the fault information,eliminating the inconsistency of the batteries,the 3-σprinciple was used as the fault trigger threshold.The experimental results based on the actual operation data of electric vehicles showed that the proposed method was more accurate and robust than the information entropy method,which could accurately locating the fault cell in the early stage of the fault,reducing the false alarm rate of inconsistent batteries faults.

关 键 词：实际运行数据 故障诊断 小波分解 广义无量纲指标(GDI) 动力电池 

分 类 号：TM912.9[电气工程—电力电子与电力传动]