摘要
故障诊断在保障电动汽车安全运行中起着至关重要的作用。针对串联电池组接触故障,提出一种基于自适应白噪声的完整经验模态分解(CEEMDAN)和模糊熵结合的串联电池组接触故障诊断方法。首先通过CEEMDAN分解电池电压信号,提取有效故障特征,然后计算移动窗口下故障特征的模糊熵,最后通过设计的故障识别策略进行实时故障诊断。仿真与实验结果表明,该算法可以准确识别串联电池组不同严重程度的接触故障,具有良好的实时性与稳定性。
Fault diagnosis plays an important role in ensuring the safe operation of electric vehicles.For the contact fault of series battery pack,a series battery pack contact fault diagnosis algorithm based on adaptive white noise complete empirical mode decomposition(CEEMDAN)and fuzzy entropy is proposed.Firstly,the battery voltage signal is decomposed by CEEMDAN to extract the effective fault features,and the fuzzy entropy of the fault features under the moving window is calculated.Finally,the real-time fault diagnosis is carried out through the designed fault identification strategy.The simulation and experimental results show that the algorithm can accurately identify the contact faults of series battery packs with different severity,and has good stability and real-time performance.
作者
李来宝
肖占龙
孙跃东
Li Laibao;Xiao Zhanlong;Sun Yuedong(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《农业装备与车辆工程》
2022年第8期150-154,共5页
Agricultural Equipment & Vehicle Engineering
