电子测量与仪器学报

范天娥,唐鑫,雷浩然,李鹏华.统计分析和密度聚类的锂离子电池组内短路故障诊断[J].电子测量与仪器学报,2023,37(7):93-103

统计分析和密度聚类的锂离子电池组内短路故障诊断

Internal short-circuit fault diagnosis of lithium-ion battery pack based on statistical analysis and density clustering

DOI：

中文关键词: 电池组短路故障诊断相对熵相关系数 DBSCAN

英文关键词:battery short-circuit fault diagnosis relative entropy correlation coefficient DBSCAN

基金项目:国家自然科学基金( 52272388)、中国博士后科学基金面上项目( 2021M693766)、重庆市自然科学基金面上项目( cstc2021jcyjmsxmX0503)资助

作者	单位
范天娥	1. 重庆邮电大学自动化学院，2. 工业物联网与网络控制教育部重点实验室
唐鑫	1. 重庆邮电大学自动化学院
雷浩然	1. 重庆邮电大学自动化学院
李鹏华	1. 重庆邮电大学自动化学院，2. 工业物联网与网络控制教育部重点实验室

Author	Institution
Fan Tian′e	1. College of Automation, Chongqing University of Posts and Telecommunications，2. Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications
Tang Xin	1. College of Automation, Chongqing University of Posts and Telecommunications
Lei Haoran	1. College of Automation, Chongqing University of Posts and Telecommunications
Li Penghua	1. College of Automation, Chongqing University of Posts and Telecommunications，2. Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications

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中文摘要:

随着锂离子电池系统在电动汽车中的广泛应用,电池组短路引起的安全问题日益凸显,因此动力电池的状态监测与故障诊断备受关注。针对当前非模型故障诊断方法存在的泛用性低、抗干扰性差和电池组不一致性突出等问题,提出了一种基于统计分析和密度聚类的电池组短路故障诊断方法。首先根据遗忘机制,利用核密度估计的相对熵和相关系数提取电池组的故障信息,用于识别短路引起的电池电压和温度变化;接着采用基于密度的空间噪声聚类算法(DBSCAN)自动识别短路故障电池。该方法的鲁棒性在噪声干扰和电池组较大不一致性的条件下得到了有效验证。随后,在不同程度的微短路情况下(短路电阻分别为 1、5 和 10 Ω)进行故障诊断,结果表明在 10 Ω 短路情况下故障诊断的准确率能够达到 92. 17%。最后通过对比分析,表明该诊断方法能够有效检测和定位短路电池,并且故障越严重,诊断所需时间越短。

英文摘要:

With the wide application of lithium-ion battery systems in electric vehicles, the safety issue caused by short-circuit fault of battery pack is becoming more serious. Therefore, the studies on state monitoring of battery pack and fault diagnosis are receiving more attention. To deal with the issues of low generality, poor anti-interference capacity and critical inconsistency of battery pack existed in non-model-based fault diagnosis methods, a short-circuit fault diagnosis method based on statistical analysis and density clustering is proposed for battery packs in this paper. Firstly, the fault information of battery pack is extracted by using the relative entropy of kernel density estimation (KDE) and correlation coefficient, based on a forgetting mechanism. The fault information is used to identify the changes of batteries’ voltage and temperature caused by short-circuit fault. Then, the short-circuit battery can be automatically identified by adopting the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The robustness of the proposed method is validated under conditions of noise interference and serious inconsistency. Furthermore, the effectiveness of the proposed method is verified under different short-circuit degree with 1, 5 and 10 Ω short-circuit resistors, and the accuracy of short-circuit fault diagnosis can reach 92. 17% in the case of a 10 Ω short-circuit resistor. By comparative analysis, the results show that the proposed diagnosis method can effectively detect and locate short-circuit batteries, and the more severe the fault, the shorter the diagnosis time required.

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