基于1dCNN-LSTM量化单体异常性的动力电池故障诊断方法  

Fault Diagnosis Method for Power Battery Based on Quantification of Cell Abnormality with 1dCNN-LSTM

作　　者：陈吉清[1,2] 冯雨佳 兰凤崇[1,2] 王平[1,2] Chen Jiqing;Feng Yujia;Lan Fengchong;Wang Ping(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640;South China University of Technology,Guangdong Provincial Key Laboratory of Automotive Engineering,Guangzhou 510640)

机构地区：[1]华南理工大学机械与汽车工程学院,广州510640 [2]华南理工大学,广东省汽车工程重点实验室,广州510640

出　　处：《汽车工程》2024年第7期1177-1188,共12页Automotive Engineering

基　　金：国家车辆事故深度调查体系(NAIS);新能源汽车事故调查协作网项目资助。

摘　　要：准确的动力电池单体性能评估对保障动力电池安全具有重要意义。目前基于数据驱动的电池故障诊断算法,大多对各单体电池进行相互比较,根据各单体电压等特征参数之间的差异,使用分类算法将离群单体认定为故障单体。然而当动力电池包内有多个异常表现相似的电池单体,或所有单体性能整体恶化时,难以区分甚至没有显著离群的个别单体,相互比较策略的应用范围受到限制。本文提出了一种基于1dCNN-LSTM量化单体异常性的动力电池故障诊断方法,结合车辆运动状态、驱动系统状态及动力电池电信号3类特征,建立1dCNN-LSTM融合模型估计理想状态下的单体实时电压参考值,根据各单体电压实测值与参考值之间的差异,量化各单体异常性。结合实际案例表明,对于因单体故障导致热失控的案例,本方法可以提前7日识别故障单体相比其他单体的明显异常,且可以在距离事故发生1年前甚至更早的放电片段中发现潜在风险;针对无明显单体不一致的整体恶化案例,可以实现事故发生前7日内的整体性能恶化过程跟踪。Accurate performance evaluation of power battery cells is of great significance to ensuring the safety of power batteries.For the existing data-driven battery fault diagnosis algorithms,mostly individual cells are compared with each other and the outlier cells are identified as faulty cells by classification,based on differences in characteristic parameters such as single cell voltage.However,if there are multiple cells of similar abnormally per-formance in the power battery pack,or all individual batteries show an overall performance deterioration,it is diffi-cult to distinguish individual cells or even there is no significant outliers,and the application of the mutual compari-son strategy is limited.A power battery fault diagnosis method is proposed based on 1dCNN-LSTM to quantify the abnormality of a single cell in this paper.Combining the three types of characteristics of vehicle motion status,drive system status and power battery electrical signal,the 1dCNN-LSTM fusion model is established to estimate the indi-vidual cell voltage under ideal conditions as reference.The difference between the real-time voltage reference value and the measured voltage value is used to quantify the abnormality of each cell.Combined with actual cases,it is shown that for thermal runaway case due to single cell failure,the abnormal performance of the faulty cell compared to others can be identified 7 days before accident,and potential risk can be recognized in discharge processes from a year of more before the accident.For overall deterioration cases without obvious individual cells inconsistency,the deterioration evolution within the last 7 days can be tracked.

关 键 词：动力电池 故障诊断 单体不一致性 融合模型 实时电压估计 

分 类 号：U469.72[机械工程—车辆工程] U472.9[交通运输工程—载运工具运用工程]