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
  • [1] Fault diagnosis of lithium-ion battery sensors based on multi-method fusion
    Yan, Yuan
    Luo, Wei
    Wang, Zhifu
    Xu, Song
    Yang, Zhongyi
    Zhang, Shunshun
    Hao, Wenmei
    Lu, Yanxi
    JOURNAL OF ENERGY STORAGE, 2024, 85
  • [2] A Novel Method for Lithium-Ion Battery Fault Diagnosis of Electric Vehicle Based on Real-Time Voltage
    Li, Fang
    Min, Yongjun
    Zhang, Ying
    WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022, 2022
  • [3] An Early Multi-Fault Diagnosis Method of Lithium-ion Battery Based on Data-Driven
    Gu, Xin
    Shang, Yunlong
    Li, Chijun
    Zhu, Yuhao
    Duan, Bin
    Li, Jinglun
    Zhao, Wenyuan
    2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 5206 - 5210
  • [4] A novel fault diagnosis method for lithium-Ion battery packs of electric vehicles
    Li, Xiaoyu
    Wang, Zhenpo
    MEASUREMENT, 2018, 116 : 402 - 411
  • [5] A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles
    Xiong, Rui
    Yu, Quanqing
    Shen, Weixiang
    Lin, Cheng
    Sun, Fengchun
    IEEE TRANSACTIONS ON POWER ELECTRONICS, 2019, 34 (10) : 9709 - 9718
  • [6] Data-Driven Fault Diagnosis of Lithium-Ion Battery Overdischarge in Electric Vehicles
    Gan, Naifeng
    Sun, Zhenyu
    Zhang, Zhaosheng
    Xu, Shiqi
    Liu, Peng
    Qin, Zian
    IEEE TRANSACTIONS ON POWER ELECTRONICS, 2022, 37 (04) : 4575 - 4588
  • [7] Battery fault diagnosis methods for electric vehicle lithium-ion batteries: Correlating codes and battery management system
    Naresh, G.
    Praveenkumar, T.
    Madheswaran, Dinesh Kumar
    Varuvel, Edwin Geo
    Pugazhendhi, Arivalagan
    Thangamuthu, Mohanraj
    Muthiya, S. Jenoris
    PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2025, 196
  • [8] Lithium-ion battery modeling based on Big Data
    Li, Shuangqi
    Li, Jianwei
    He, Hongwen
    Wang, Hanxiao
    RENEWABLE ENERGY INTEGRATION WITH MINI/MICROGRID, 2019, 159 : 168 - 173
  • [9] 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
  • [10] Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles
    Liu, Zhentong
    He, Hongwen
    ENERGIES, 2015, 8 (07): : 6509 - 6527