断层(地质)
电池(电)
时频分析
小波变换
小波
图表
电压
功率(物理)
工程类
信号(编程语言)
熵(时间箭头)
聚类分析
计算机科学
控制理论(社会学)
电子工程
算法
人工智能
电气工程
物理
地质学
滤波器(信号处理)
数据库
地震学
量子力学
程序设计语言
控制(管理)
作者
Chun Chang,Qiyue Wang,Jiuchun Jiang,Yan Jiang,Tiezhou Wu
出处
期刊:Energy
[Elsevier]
日期:2023-09-01
卷期号:278: 127920-127920
被引量:3
标识
DOI:10.1016/j.energy.2023.127920
摘要
A fault diagnosis method for electric vehicle power batteries based on a time-frequency diagram is proposed. First, the original voltage signal is decomposed by improved variational mode decomposition to eliminate the influence of battery inconsistency on battery feature extraction. Then, the continuous wavelet transform is used to transform the one-dimensional signal into a two-dimensional time-frequency diagram, and the image entropy is used to reflect the characteristic parameters of the battery fault. Finally, the abnormal battery is marked with clustering algorithm. It is verified by real vehicle data that the proposed method can identify the battery fault and advance the identification time.
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