IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8458-d970756.html
   My bibliography  Save this article

State-Partial Accurate Voltage Fault Prognosis for Lithium-Ion Batteries Based on Self-Attention Networks

Author

Listed:
  • Huaqin Zhang

    (Key Laboratory of Conveyance Equipment, Ministry of Education, East China Jiaotong University, Nanchang 330013, China
    School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Shunde Innovation School, University of Science and Technology Beijing, Foshan 528000, China)

  • Jichao Hong

    (Key Laboratory of Conveyance Equipment, Ministry of Education, East China Jiaotong University, Nanchang 330013, China
    School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Shunde Innovation School, University of Science and Technology Beijing, Foshan 528000, China)

  • Zhezhe Wang

    (School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Shunde Innovation School, University of Science and Technology Beijing, Foshan 528000, China)

  • Guodong Wu

    (Key Laboratory of Conveyance Equipment, Ministry of Education, East China Jiaotong University, Nanchang 330013, China)

Abstract

Multiple faults in new energy vehicle batteries can be diagnosed using voltage. To find voltage fault information in advance and reduce battery safety risk, a state-partitioned voltage fault prognosis method based on the self-attention network is proposed. The voltage data are divided into three parts with typical characteristics according to the charging voltage curve trends under different charge states. Subsequently, a voltage prediction model based on the self-attention network is trained separately with each part of the data. The voltage fault prognosis is realized using the threshold method. The effectiveness of the method is verified using real operating data of electric vehicles (EVs). The effects of different batch sizes and window sizes on model training are analyzed, and the optimized hyperparameters are used to train the voltage prediction model. The average error of predicted voltage is less than 2 mV. Finally, the superiority and robustness of the method are verified.

Suggested Citation

  • Huaqin Zhang & Jichao Hong & Zhezhe Wang & Guodong Wu, 2022. "State-Partial Accurate Voltage Fault Prognosis for Lithium-Ion Batteries Based on Self-Attention Networks," Energies, MDPI, vol. 15(22), pages 1-14, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8458-:d:970756
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8458/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/22/8458/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    2. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Sun, Zhenyu & Han, Yang & Wang, Zhenpo & Chen, Yong & Liu, Peng & Qin, Zian & Zhang, Zhaosheng & Wu, Zhiqiang & Song, Chunbao, 2022. "Detection of voltage fault in the battery system of electric vehicles using statistical analysis," Applied Energy, Elsevier, vol. 307(C).
    4. Enguang Hou & Yanliang Xu & Xin Qiao & Guangmin Liu & Zhixue Wang, 2021. "State of Power Estimation of Echelon-Use Battery Based on Adaptive Dual Extended Kalman Filter," Energies, MDPI, vol. 14(17), pages 1-14, September.
    5. Christensen, Paul A. & Anderson, Paul A. & Harper, Gavin D.J. & Lambert, Simon M. & Mrozik, Wojciech & Rajaeifar, Mohammad Ali & Wise, Malcolm S. & Heidrich, Oliver, 2021. "Risk management over the life cycle of lithium-ion batteries in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    6. Yang, Jufeng & Cai, Yingfeng & Mi, Chris, 2022. "Lithium-ion battery capacity estimation based on battery surface temperature change under constant-current charge scenario," Energy, Elsevier, vol. 241(C).
    7. Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(C).
    8. Hong, Jichao & Wang, Zhenpo & Yao, Yongtao, 2019. "Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Shi, Haotian & Wang, Shunli & Fernandez, Carlos & Yu, Chunmei & Xu, Wenhua & Dablu, Bobobee Etse & Wang, Liping, 2022. "Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries," Applied Energy, Elsevier, vol. 324(C).
    10. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
    11. Hong, Jichao & Wang, Zhenpo & Qu, Changhui & Zhou, Yangjie & Shan, Tongxin & Zhang, Jinghan & Hou, Yankai, 2022. "Investigation on overcharge-caused thermal runaway of lithium-ion batteries in real-world electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hong, Jichao & Liang, Fengwei & Chen, Yingjie & Wang, Facheng & Zhang, Xinyang & Li, Kerui & Zhang, Huaqin & Yang, Jingsong & Zhang, Chi & Yang, Haixu & Ma, Shikun & Yang, Qianqian, 2024. "A novel battery abnormality diagnosis method using multi-scale normalized coefficient of variation in real-world vehicles," Energy, Elsevier, vol. 299(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    2. E, Jiaqiang & Xiao, Hanxu & Tian, Sicheng & Huang, Yuxin, 2024. "A comprehensive review on thermal runaway model of a lithium-ion battery: Mechanism, thermal, mechanical, propagation, gas venting and combustion," Renewable Energy, Elsevier, vol. 229(C).
    3. Liu, Yunpeng & Hou, Bo & Ahmed, Moin & Mao, Zhiyu & Feng, Jiangtao & Chen, Zhongwei, 2024. "A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments," Applied Energy, Elsevier, vol. 358(C).
    4. Li, Lin & Zhang, Tiezhu & Lu, Liqun & Zhang, Hongxin & Yang, Jian & Zhang, Zhen, 2023. "An energy active regulation management strategy based on driving mode recognition for electro-hydraulic hybrid vehicles," Energy, Elsevier, vol. 285(C).
    5. He, Ning & Wang, Qiqi & Lu, Zhenfeng & Chai, Yike & Yang, Fangfang, 2024. "Early prediction of battery lifetime based on graphical features and convolutional neural networks," Applied Energy, Elsevier, vol. 353(PA).
    6. Song, Dengwei & Cheng, Yujie & Zhou, An & Lu, Chen & Chong, Jin & Ma, Jian, 2024. "Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    7. Hong, Jichao & Zhang, Huaqin & Zhang, Xinyang & Yang, Haixu & Chen, Yingjie & Wang, Facheng & Huang, Zhongguo & Wang, Wei, 2024. "Online accurate voltage prediction with sparse data for the whole life cycle of Lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 369(C).
    8. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    9. Wang, Cong & Chen, Yunxia & Zhang, Qingyuan & Zhu, Jiaxiao, 2023. "Dynamic early recognition of abnormal lithium-ion batteries before capacity drops using self-adaptive quantum clustering," Applied Energy, Elsevier, vol. 336(C).
    10. Kurucan, Mehmet & Ă–zbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    11. Hong, Jichao & Liang, Fengwei & Chen, Yingjie & Wang, Facheng & Zhang, Xinyang & Li, Kerui & Zhang, Huaqin & Yang, Jingsong & Zhang, Chi & Yang, Haixu & Ma, Shikun & Yang, Qianqian, 2024. "A novel battery abnormality diagnosis method using multi-scale normalized coefficient of variation in real-world vehicles," Energy, Elsevier, vol. 299(C).
    12. Zhang, Zhen & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Yang, Jian & Jia, Qingxiao, 2023. "Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle," Energy, Elsevier, vol. 269(C).
    13. Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    14. Li, Lin & Zhang, Tiezhu & Sun, Binbin & Wu, Kaiwei & Sun, Zehao & Zhang, Zhen & Lin, Lianhua & Xu, Haigang, 2023. "Research on electro-hydraulic ratios for a novel mechanical-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 270(C).
    15. Chen, Zhang & Chen, Liqun & Ma, Zhengwei & Xu, Kangkang & Zhou, Yu & Shen, Wenjing, 2023. "Joint modeling for early predictions of Li-ion battery cycle life and degradation trajectory," Energy, Elsevier, vol. 277(C).
    16. Shi, Haotian & Wang, Shunli & Huang, Qi & Fernandez, Carlos & Liang, Jianhong & Zhang, Mengyun & Qi, Chuangshi & Wang, Liping, 2024. "Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries," Applied Energy, Elsevier, vol. 353(PB).
    17. Ming Zhang & Dongfang Yang & Jiaxuan Du & Hanlei Sun & Liwei Li & Licheng Wang & Kai Wang, 2023. "A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms," Energies, MDPI, vol. 16(7), pages 1-28, March.
    18. Deng, Zhihua & Chan, Siew Hwa & Chen, Qihong & Liu, Hao & Zhang, Liyan & Zhou, Keliang & Tong, Sirui & Fu, Zhichao, 2023. "Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system," Applied Energy, Elsevier, vol. 331(C).
    19. Cui, Binghan & Wang, Han & Li, Renlong & Xiang, Lizhi & Zhao, Huaian & Xiao, Rang & Li, Sai & Liu, Zheng & Yin, Geping & Cheng, Xinqun & Ma, Yulin & Huo, Hua & Zuo, Pengjian & Lu, Taolin & Xie, Jingyi, 2024. "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model," Applied Energy, Elsevier, vol. 353(PA).
    20. Jinrui Nan & Bo Deng & Wanke Cao & Jianjun Hu & Yuhua Chang & Yili Cai & Zhiwei Zhong, 2022. "Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation," Energies, MDPI, vol. 15(15), pages 1-19, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8458-:d:970756. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.