稳健性(进化)
电压
计算机科学
断层(地质)
规范化(社会学)
波形
过电压
相似性(几何)
算法
工程类
电气工程
人工智能
地震学
地质学
生物化学
化学
社会学
人类学
图像(数学)
基因
作者
Qifan Yang,Hongzhong Ma,Jinlei Sun,Yongzhe Kang,Dawei Duan,Ping Ju
出处
期刊:IEEE Transactions on Transportation Electrification
日期:2022-05-04
卷期号:9 (1): 1008-1020
被引量:7
标识
DOI:10.1109/tte.2022.3172663
摘要
Electrical faults pose a serious threat to the safe operation of battery packs. Common electrical faults include undervoltage, overvoltage, connection faults, and sensor faults. However, existing methods fail to provide a comprehensive and adequate diagnosis of the four types of electrical faults due to their inability to distinguish between fault signatures. This article proposes an online multifault diagnosis scheme based on voltage envelopes. First, using the positive and negative envelopes of voltages, unique fault signatures are constructed for each type of electrical fault, where the similarity and polarity of the envelopes are selected as indicators. Then, an improved Hausdorff distance and a cross-voltage measurement circuit are employed to catch the fault signatures. By comparing the similarity and polarity between the adjacent envelopes, four types of faults can be detected and sequentially distinguished. Finally, an online diagnosis flow is designed, where normalization and the Douglas–Peucker algorithm are used to ensure robustness to inconsistency and noise. Experimental results show that the four types of faults can be accurately diagnosed within a short period of time, and a minor fault characterized by a 0.07-C discharge current can still be sensitively detected. In addition, the inconsistency and noise within the normal range cannot cause a misdiagnosis.
科研通智能强力驱动
Strongly Powered by AbleSci AI