Fault Diagnosis of Lithium-Ion Batteries Based on the Historical Trajectory of Remaining Discharge Capacity

被引:0
|
作者
Jiang, Jiuchun [1 ]
Qu, Bingrui [2 ]
Liu, Shuaibang [1 ]
Yan, Huan [2 ]
Zhang, Zhen [2 ]
Chang, Chun [2 ]
机构
[1] Beijing Inst Technol, Shenzhen Automot Res Inst, Shenzhen 518118, Peoples R China
[2] Hubei Univ Technol, Hubei Key Lab High Efficiency Utilizat Solar Energ, Wuhan 430000, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 23期
关键词
lithium-ion battery; fault diagnosis; medium and long time scale; historical trajectory;
D O I
10.3390/app142310895
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In recent years, the number of safety accidents in new-energy electric vehicles due to lithium-ion battery failures has been increasing, and the lithium-ion battery fault diagnosis technology is particularly important to ensure the safe operation of electric vehicles. This paper proposes a method for lithium-ion battery fault diagnosis based on the historical trajectory of lithium-ion battery remaining discharge capacity in medium and long time scales. The method first utilizes the sparrow search algorithm (SSA) to identify the parameters of the second-order equivalent circuit model of the lithium-ion battery, and then estimates the state of charge (SOC) of the lithium-ion battery using the extended Kalman filter (EKF). The remaining discharge capacity is estimated according to the SOC, and finally the feature vectors are used to diagnose the faults using box plots on the medium and long time scales. Experimental results verify that the root mean squared error (RSME) and mean absolute error (MAE) of the proposed SOC estimation method are 0.0049 and 0.0034, respectively. This method can accurately identify the faulty single cell in a battery pack with low-capacity single cells and promptly detect any abnormalities in the single cell when a micro-short circuit fault occurs.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Simulation of capacity fade in lithium-ion batteries
    Spotnitz, R
    JOURNAL OF POWER SOURCES, 2003, 113 (01) : 72 - 80
  • [32] Studies on capacity fade of lithium-ion batteries
    Zhang, D
    Haran, BS
    Durairajan, A
    White, RE
    Podrazhansky, Y
    Popov, BN
    JOURNAL OF POWER SOURCES, 2000, 91 (02) : 122 - 129
  • [33] Probabilistic Prediction of Remaining Useful Life of Lithium-ion Batteries
    Zhang, Renjie
    Li, Jialin
    Chen, Yifei
    Tan, Shiyi
    Jiang, Jiaxu
    Yuan, Xinmei
    2022 4TH INTERNATIONAL CONFERENCE ON SMART POWER & INTERNET ENERGY SYSTEMS, SPIES, 2022, : 1820 - 1824
  • [34] An Online Prediction of Capacity and Remaining Useful Life of Lithium-Ion Batteries Based on Simultaneous Input and State Estimation Algorithm
    Ouyang, Tiancheng
    Xu, Peihang
    Chen, Jingxian
    Lu, Jie
    Chen, Nan
    IEEE TRANSACTIONS ON POWER ELECTRONICS, 2021, 36 (07) : 8102 - 8113
  • [35] Outlier Mining-Based Fault Diagnosis for Multicell Lithium-Ion Batteries Using a Low-Priced Microcontroller
    Kim, Taesic
    Adhikaree, Amit
    Pandey, Rajendra
    Kang, Daewook
    Kim, Myoungho
    Oh, Chang-Yeol
    Back, Juwon
    THIRTY-THIRD ANNUAL IEEE APPLIED POWER ELECTRONICS CONFERENCE AND EXPOSITION (APEC 2018), 2018, : 3365 - 3369
  • [36] Investigation of Impulse and Continuous Discharge Characteristics of Large-Capacity Lithium-Ion Batteries
    Kuchak, Sergey V.
    Brovanov, Sergey V.
    PROCESSES, 2022, 10 (12)
  • [37] Capacity and remaining useful life prediction for lithium-ion batteries based on sequence decomposition and a deep-learning network
    Wang, Zili
    Liu, Yonglu
    Wang, Fen
    Wang, Hui
    Su, Mei
    JOURNAL OF ENERGY STORAGE, 2023, 72
  • [38] A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction
    Guo, Peiyao
    Cheng, Ze
    Yang, Lei
    JOURNAL OF POWER SOURCES, 2019, 412 : 442 - 450
  • [39] Lithium-ion battery fault diagnosis method based on KPCA-MTCN
    Tan, Qipeng
    Li, Yongqi
    Chen, Man
    Zhang, Lingxian
    Peng, Peng
    Wan, Minhui
    Gongcheng Kexue Xuebao/Chinese Journal of Engineering, 2024, 46 (12): : 2297 - 2306
  • [40] Performance Evaluation of Machine Learning and Deep Learning-Based Models for Predicting Remaining Capacity of Lithium-Ion Batteries
    Lee, Sang-Hyun
    APPLIED SCIENCES-BASEL, 2023, 13 (16):