基于局部离群点检测的动力电池组不一致早期故障预警  

Early fault warning for inconsistent power battery pack based on local outlier detection

作　　者：魏正新 吕晗珺 闵永军[1] 张涌 WEI Zhengxin;LYU Hanjun;MIN Yongjun;ZHANG Yong(College of Automobile and Traffic Engineering,Nanjing Forestry University,Nanjing 210037,China)

机构地区：[1]南京林业大学汽车与交通工程学院,南京210037

出　　处：《重庆理工大学学报（自然科学）》2024年第6期21-29,共9页Journal of Chongqing University of Technology：Natural Science

基　　金：江苏省重点研发计划项目(BE2022053-2)。

摘　　要：随着新能源汽车的飞速发展,其动力电池的安全性问题受到了社会各界的广泛关注。在新能源汽车运行监控平台上,已有的动力电池安全检测功能无法在电池故障早期给出预警。针对电池不一致性预警问题,设计了一种更适用于实现动力电池组不一致早期故障预警问题的流程。设计了一种基于箱型图法的动态梯度数据清洗策略实现异常数据有效剔除;对数据进行充电阶段划分,提取单体电压变化不一致特征;在此基础上,借助离群检测算法得到各电池单体离群值,进行不一致故障初期预警并识别异常电池单体。对实际出现电池不一致故障车辆回溯分析,验证该流程提前监控平台已有的报警机制不少于7个充电周期,并可对异常单体进行准确定位。With the rapid development of new energy vehicles,the safety of power batteries has gained growing public attention.On the new energy vehicle operation monitoring platform,the existing power battery safety detection function fails to provide early warnings of battery failures.A more suitable process for early warnings of battery inconsistency in power battery packs has been designed to address the battery inconsistency warnings.First,a dynamic gradient data cleaning strategy based on box graph method is designed to effectively eliminate abnormal data.Then,the data are divided into charging stages and inconsistent characteristics of individual voltage changes are extracted.Based on this,the outlier detection algorithm is employed to obtain the outlier values of each battery cell,conduct initial warning of inconsistent faults,and identify abnormal battery cells.Our retrospective analysis of actual vehicles with inconsistent battery faults demonstrates that the preexisting alarm mechanism of the monitoring platform for this process has no less than 7 charging cycles and accurately locates abnormal cells.

关 键 词：动力电池 大数据 离群检测 电池不一致 故障预警 

分 类 号：U469.72[机械工程—车辆工程]