基于云端数据的电池系统连接异常诊断研究  

Research on Diagnosis of Abnormal Connection in Battery Systems Based on Cloud Data

作　　者：吴二东 王澎 万鑫铭[1,2] 赵星 马留可 Wu Erdong;Wang Peng;Wan Xinming;Zhao Xing;Ma Liuke(China Automotive Engineering Research Institute Co.,Ltd,Chongqing 401122;China Inspection and Certification Group Inspection Co.,Ltd,Beijing 100053)

机构地区：[1]中国汽车工程研究院股份有限公司,重庆401122 [2]中国检验认证集团,北京100053

出　　处：《汽车技术》2024年第9期46-50,共5页Automobile Technology

基　　金：国家市场监督管理总局科技计划项目(2022MK106);国家重点研发计划项目(2021YFF0601100)。

摘　　要：为有效识别新能源汽车电池系统连接异常问题,利用应急预警云端监测平台和大数据分析方法,结合正常车辆和连接异常车辆的数据模式异同,挖掘电池系统连接异常缺陷因素。提出一种基于数据驱动的新能源汽车电池系统连接异常风险因子识别算法,根据风险因子对电池系统连接异常程度进行等级划分,结果表明,所提出算法可以准确有效识别连接异常高风险车辆。It is crucial to effectively identify abnormal connections in the battery system of new energy vehicles in order to address their operational safety issues.By utilizing an emergency warning cloud monitoring platform and big data analysis methods,combined with the similarities and differences in data patterns between normal vehicles and vehicles with abnormal or faulty connections,this paper aim.to explore the factors contributing to abnormal defects in power battery connections.A datadriven algorithm for identifying abnormal risk factors in the connection of new energy vehicle battery systems is developed.According to the risk factors,the degree of abnormal connection in the battery system is classified into different levels,and the results show that the proposed algorithm can accurately and effectively identify high-risk vehicles with abnormal connections.

关 键 词：连接异常 云端平台 故障诊断 大数据 

分 类 号：TM911[电气工程—电力电子与电力传动]