汽车工程 ›› 2024, Vol. 46 ›› Issue (7): 1167-1176.doi: 10.19562/j.chinasae.qcgc.2024.07.004

• • 上一篇    

基于电化学阻抗谱的电池组不一致性诊断

姚晗欣1,2,王学远1,2(),袁永军1,2,3,戴海峰1,2(),魏学哲1,2   

  1. 1.同济大学汽车学院,上海 201804
    2.同济大学新能源汽车工程中心,上海 201804
    3.上海炙云新能源科技有限公司,上海 201806
  • 收稿日期:2023-10-22 修回日期:2023-12-18 出版日期:2024-07-25 发布日期:2024-07-22
  • 通讯作者: 王学远,戴海峰 E-mail:7wangxueyuan@tongji.edu.cn;tongjidai@tongji.edu.cn
  • 基金资助:
    国家自然科学基金(52207242);汽车安全与节能国家重点实验室开放基金(KFY2226)资助

Diagnosis for Battery Module Inconsistencies Based on Electrochemical Impedance Spectroscopy

Hanxin Yao1,2,Xueyuan Wang1,2(),Yongjun Yuan1,2,3,Haifeng Dai1,2(),Xuezhe Wei1,2   

  1. 1.School of Automotive Studies,Tongji University,Shanghai  201804
    2.Clean Energy Automotive Engineering Center,Tongji University,Shanghai  201804
    3.Shanghai Fire Cloud New Energy Technology Co. ,Ltd. ,Shanghai  201806
  • Received:2023-10-22 Revised:2023-12-18 Online:2024-07-25 Published:2024-07-22
  • Contact: Xueyuan Wang,Haifeng Dai E-mail:7wangxueyuan@tongji.edu.cn;tongjidai@tongji.edu.cn

摘要:

电池组中的单体电池之间可能会存在温度、荷电状态、老化状态(容量和内阻)等不一致。由于“短板效应”的存在,不一致性将会影响电池组的整体性能发挥,及时准确地进行不一致性诊断非常必要。考虑到上述提及的不一致性会对电极过程特性产生影响,进而反映在电化学阻抗谱(EIS)和弛豫时间分布(DRT)上,本文在结合等效电路厘清几种不一致性对EIS和DRT的影响规律后,创新性地提出了一种基于EIS和DRT的电池组不一致性诊断方法。通过将异常电池混入一组一致性良好的电池中,对比分析了K-means、AP和DBSCAN等无监督聚类算法性能,结果表明DBSCAN诊断准确率为99.2%,可以实现电池组内单体电池不一致性差异的准确诊断。

关键词: 锂离子电池, 电化学阻抗谱, 弛豫时间分布, 不一致性, 无监督聚类

Abstract:

There may be inconsistencies in temperature, charge state, aging state (capacity and internal resistance) between individual cells in a battery module. Due to the existence of the "short board effect", the inconsistencies will affect the overall performance of the battery module, so timely and accurate inconsistencies diagnosis is very necessary. Considering that the above-mentioned inconsistencies will affect the electrode process characteristics, which will be reflected in the Electrochemical Impedance Spectroscopy (EIS) and Distribution of Relaxation Time (DRT), in this paper, after clarifying the effect of several kinds of inconsistencies on EIS and DRT by combining the equivalent circuits, an inconsistencies diagnosis method for battery modules based on EIS and DRT is innovatively proposed. The performance of unsupervised clustering algorithms such as K-means, AP (Affinity Propagation) and DBSCAN (Density Based Spatial Clustering of Applications with Noise) is comparatively analyzed by mixing the abnormal batteries into a group of batteries with good consistency. The results show that the DBSCAN diagnostic accuracy is 99.2%, which can realize the accurate diagnosis of the inconsistency difference of single cells within the battery module.

Key words: lithium-ion batteries, electrochemical impedance spectroscopy, distribution of relaxation time, inconsistency, unsupervised clustering