基于历史数据的车载锂离子电池异常诊断

doi:10.3901/JME.2023.22.089

机械工程学报 ›› 2023, Vol. 59 ›› Issue (22): 89-99.doi: 10.3901/JME.2023.22.089

• 特邀专栏：动力电池安全应用技术 • 上一篇下一篇

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基于历史数据的车载锂离子电池异常诊断

张言茹^1,2, 张珺玮^1,2, 王占国^1,2, 李劼峰^1,2, 张维戈^1,2

1. 北京交通大学电气工程学院北京 100044;
2. 国家能源主动配电网技术研发中心北京 100044

收稿日期:2022-11-13 修回日期:2023-04-26 出版日期:2023-11-20 发布日期:2024-02-19
通讯作者: 张维戈(通信作者)，男，1971年出生，博士，教授，博士研究生导师。主要研究方向为电力电子技术，电池管理技术等。E-mail：wgzhang@bjtu.edu.cn
作者简介:张言茹，女，1990年出生，实验师。主要研究方向为动力电池成组技术。E-mail：yr_zhang@bjtu.edu.cn
基金资助:
北京市自然科学基金资助项目(3212033)。

Abnormal Diagnosis of Vehicle Lithium Ion Battery Based on Historical Data

ZHANG Yanru^1,2, ZHANG Junwei^1,2, WANG Zhanguo^1,2, LI Jiefeng^1,2, ZHANG Weige^1,2

1. School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044;
2. National Active Distribution Network Technology Research Center, Beijing 100044

Received:2022-11-13 Revised:2023-04-26 Online:2023-11-20 Published:2024-02-19

摘要/Abstract

摘要： 由于车载动力电池的劣化存在时变性、路径依赖性及不一致导致的随机性，通过后台历史运行数据判断电池异常，识别电池安全风险逐渐成为技术发展趋势。目前针对历史数据的异常诊断存在忽略电池老化时变性和对等效模型及循环数据过度依赖等问题。通过组内不一致性分析，基于统计学迭代筛选异常电池并提取健康电池参考电压；建立偏离指数表征模型，分析单次充电过程中电压离群变化规律，从短时间尺度诊断电池异常程度和类型；基于变异系数法，构建全历史过程综合偏离指数矩阵，从长时间维度的突变性判断电池异常变化速度。故障实例及批量分析结果表明，该异常诊断方法不仅可以准确诊断电池包中的异常电池，还可有效对劣化趋势进行量化，为故障预警和合理化干预措施提供依据。

关键词: 锂离子电池, 历史数据, 异常诊断, 容量, 荷电状态

Abstract: Due to the randomness caused by time variability, path dependence and inconsistency of power battery degradation, it is a trend to judge battery abnormalities and identify battery safety risks through background historical operation data. At present, the fault diagnosis for historical data has the problems of ignoring the time-varying battery aging and excessive dependence on the equivalent model and cycle data. Abnormal batteries are screened based on statistical iteration and healthy battery reference voltage is extracted through inconsistency analysis in series-battery pack. By establishing the deviation index representation model, the variation law of voltage outliers during a single charging process is analyzed, and the degree and type of battery are diagnosed abnormality from a short time scale. Based on the coefficient of variation method, a comprehensive deviation index matrix for the entire historical process is constructed, and the abnormal change speed of the battery is judged from the abrupt change of the long-term dimension. The fault examples and batch analysis results show that the abnormality diagnosis method proposed can not only accurately diagnose abnormal batteries in the battery pack, but also effectively quantify the deterioration trend, providing a basis for fault early warning and rationalized intervention measures.

Key words: lithium-ion battery, historical data, abnormal diagnosis, capacity, state of charge

中图分类号:

TG156

张言茹, 张珺玮, 王占国, 李劼峰, 张维戈. 基于历史数据的车载锂离子电池异常诊断[J]. 机械工程学报, 2023, 59(22): 89-99.

ZHANG Yanru, ZHANG Junwei, WANG Zhanguo, LI Jiefeng, ZHANG Weige. Abnormal Diagnosis of Vehicle Lithium Ion Battery Based on Historical Data[J]. Journal of Mechanical Engineering, 2023, 59(22): 89-99.

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基于历史数据的车载锂离子电池异常诊断

Abnormal Diagnosis of Vehicle Lithium Ion Battery Based on Historical Data

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