摘要
动力电池单体不一致性故障是电动汽车的动力电池系统重要故障之一,严重影响动力电池性能和电动汽车的安全运行.为此,本文依托车企监控平台数据,提出了一种基于OPTICS聚类和电压异常指数的动力电池单体不一致性故障程度分析方法.首先,计算电压的离散弗雷歇距离和电压偏差值作为故障诊断的特征参数;其次,使用OPTICS算法对故障特征进行聚类分析,并基于统计确定的故障阈值,实现潜在异常单体的识别.最后,计算异常单体的电压异常指数,从而定量评估电池的故障程度.研究结果表明:相比基于动态K值的K-means++模型和DBSCAN模型,该方法能够准确识别“电池单体一致性差”报警车辆的所有潜在异常单体,结果更可靠;另外,相比车企监控平台报警时间,该方法最快提前近7 d发现故障;该方法还可定量分析动力电池的故障程度,且故障程度与电压异常指数成正比.本文研究成果具有较高的工程应用价值.
The impact of power battery inconsistency is a significant factor contributing to battery system failure.This inconsistency severely hampers the performance of power batteries and the safe operation of electric vehicles.To address this issue,we propose a method for analyzing the degree of inconsistent faults in power battery cells.Our method leverages ordering points to identify the clustering structure(OPTICS)clustering and voltage anomaly index,using data sourced from automobile enterprise monitoring platforms.We begin by calculating the discrete Fréchet distance and voltage deviation as characteristic parameters for fault diagnosis.Following this,we employ the OPTICS algorithm to cluster these fault features.Using statistical methods,we determine a fault threshold that enables us to accurately identify potential anomalous monomers.The final step involves calculating the voltage anomaly index of the abnormal cell to quantitatively evaluate the battery’s fault degree.Our results demonstrate that this diagnosis method does not generate false alarms for normal vehicles,while it accurately identifies all potential abnormal units in vehicles flagged with a“poor battery unit consistency”alarm.When compared with the monitoring platforms of automobile enterprises,our method can detect faults nearly 7 d in advance at the earliest.Moreover,the reliability of our diagnostic results surpasses those of the K-Means++model and the model describing density-based spatial clustering of applications with noise(DBSCAN)even when they use a dynamic K value.This suggests that our method holds substantial promise for application in engineering.
作者
单丰武
汪超仁
曾建邦
饶胤坤
刘星
李嘉成
SHAN FengWu;WANG ChaoRen;ZENG JianBang;RAO YinKun;LIU Xing;LI JiaCheng(College of Automotive Studie,Tongji University,Shanghai 200092,China;Key Laboratory of Conveyance and Equipment,East China Jiaotong University,Ministry of Education,Nanchang 330013,China;New Energy Vehicle Corporation,Jiangxi Jiangling Motors Group,Nanchang 330013,China)
出处
《中国科学：技术科学》
EI
CSCD
北大核心
2024年第8期1611-1624,共14页
Scientia Sinica(Technologica)
基金
国家自然科学基金项目(批准号:51206171)
江西省重点研发计划(编号:20223BBE51016)
载运工具与装备教育部重点实验室基金(编号:KLCE2021-08)
江西省创新创业大学生训练计划省级重点项目(编号:S202210404029)资助。
作者简介
曾建邦,E-mail:jbzeng68@sina.com。
