Electric Vehicle Lithium-Ion Battery Fault Diagnosis Based on Multi-Method Fusion of Big Data

被引:18
|
作者
Wang, Zhifu [1 ,2 ]
Luo, Wei [2 ]
Xu, Song [1 ]
Yan, Yuan [1 ]
Huang, Limin [3 ]
Wang, Jingkai [1 ]
Hao, Wenmei [1 ]
Yang, Zhongyi [2 ]
机构
[1] Beijing Inst Technol, Sch Mech & Vehicle Engn, Beijing 100081, Peoples R China
[2] Guangxi Univ Sci & Technol, Sch Automat, Liuzhou 545000, Peoples R China
[3] ChengDu Univ, Sch Mech Engn, Chengdu 610106, Peoples R China
基金
中国国家自然科学基金;
关键词
lithium-ion battery; electric vehicle; real-world vehicle data; fault diagnosis; data-driven; machine learning;
D O I
10.3390/su15021120
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Power batteries are the core of electric vehicles, but minor faults can easily cause accidents; therefore, fault diagnosis of the batteries is very important. In order to improve the practicality of battery fault diagnosis methods, a fault diagnosis method for lithium-ion batteries in electric vehicles based on multi-method fusion of big data is proposed. Firstly, the anomalies are removed and early fault analysis is performed by t-distribution random neighborhood embedding (t-Sne) and wavelet transform denoising. Then, different features of the vehicle that have a large influence on the battery fault are identified by factor analysis, and the faulty features are extracted by a two-way long and short-term memory network method with convolutional neural network. Finally a self-learning Bayesian network is used to diagnose the battery fault. The results show that the method can improve the accuracy of fault diagnosis by about 12% when verified with data from different vehicles, and after comparing with other methods, the method not only has higher fault diagnosis accuracy, but also reduces the response time of fault diagnosis, and shows superiority compared to graded faults, which is more in line with the practical application of engineering.
引用
收藏
页数:18
相关论文
共 50 条
  • [21] Electric vehicle battery fault diagnosis based on statistical method
    Zhao, Yang
    Liu, Peng
    Wang, Zhenpo
    Hong, Jichao
    8TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY (ICAE2016), 2017, 105 : 2366 - 2371
  • [22] A fault diagnosis method for electric vehicle power lithium battery based on wavelet packet decomposition
    Jiang, Jiuchun
    Zhang, Ruhang
    Wu, Yutong
    Chang, Chun
    Jiang, Yan
    JOURNAL OF ENERGY STORAGE, 2022, 56
  • [23] A multi-fault advanced diagnosis method based on sparse data observers for lithium-ion batteries
    Sun, Jing
    Qiu, Yan
    Shang, Yunlong
    Lu, Gaopeng
    JOURNAL OF ENERGY STORAGE, 2022, 50
  • [24] Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features
    Zhang, Fan
    Zheng, Xiao
    Xing, Zixuan
    Wu, Minghu
    ENERGIES, 2024, 17 (07)
  • [25] Online Diagnostic Method for Health Status of Lithium-ion Battery in Electric Vehicle
    Jiang J.
    Gao Y.
    Zhang C.
    Wang Y.
    Zhang W.
    Liu S.
    Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2019, 55 (20): : 60 - 72and84
  • [26] A LabVIEW-based fault diagnosis system for lithium-ion battery
    Tang Zining
    Fang Yunzhou
    Peng Qingfeng
    2011 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2011,
  • [27] Multi-method collaborative optimization for parallel air cooling lithium-ion battery pack
    Zhang, Furen
    Zhang, Lin
    Lin, Aizhen
    Wang, Pengwei
    Liu, Peiwen
    INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2022, 46 (10) : 14318 - 14333
  • [28] Support Vector Machine Based Lithium-ion Battery Electrolyte Leakage Fault Diagnosis Method
    Zhang, Caiping
    Zhang, Pengfei
    Wang, Yubin
    Zhang, Linjing
    Hu, Jing
    Zhang, Weige
    2022 4TH INTERNATIONAL CONFERENCE ON SMART POWER & INTERNET ENERGY SYSTEMS, SPIES, 2022, : 1880 - 1886
  • [29] Multi-States Fusion based Internal Short Circuit Fault Diagnostic for Lithium-Ion Battery
    Hu, Jian
    Wei, Zhongbao
    He, Hongwen
    2021 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE), 2021, : 1712 - 1717
  • [30] Parity Space Approach for Fault Diagnosis of Lithium-ion Battery Sensor for Electric Vehicles
    Pan F.
    Ma B.
    Gao Y.
    Xu M.
    Gong D.
    Qiche Gongcheng/Automotive Engineering, 2019, 41 (07): : 831 - 838