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Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks

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  • Zhao, Jingyuan
  • Feng, Xuning
  • Wang, Junbin
  • Lian, Yubo
  • Ouyang, Minggao
  • Burke, Andrew F.

Abstract

Battery-powered electric vehicles (EVs) are poised to accelerate decarbonization in nearly every aspect of transportation. However, safety issues of commercial lithium-ion batteries related to the faults and failures in real-world applications are still serious concern. Even a small increase in risk during the battery's operational lifetime may evolve into a safety hazard-fire and explosion, named as thermal runaway, after long-term incubation. Modelling and predicting the evolution of nonlinear multiscale electrochemical systems is challenging due to uncertainties in materials and manufacturing processes, dynamic environmental and operating conditions, as well as a lack of high-quality datasets. This challenge is further complicated when solving real-life physical problems with missing and noisy data and uncertain boundary conditions. In this study, we address these challenges by developing a specialized Transformer network architecture called BERTtery (Bidirectional Encoder Representations from Transformers for batteries) based on field data of EVs. By using charging voltage and temperature curves from early cycles before exhibiting symptoms of battery, the two-tower Transformer with temporal-wise encoder and channel-wise encoder is demonstrated as a powerful tool to capture early-warning signals across multiple spatio-temporal scales under a wide range of operating conditions. The method reliably predicts the evolution of faults in battery systems using only data provided by the onboard sensor measurements of battery performance.

Suggested Citation

  • Zhao, Jingyuan & Feng, Xuning & Wang, Junbin & Lian, Yubo & Ouyang, Minggao & Burke, Andrew F., 2023. "Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923013132
    DOI: 10.1016/j.apenergy.2023.121949
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    References listed on IDEAS

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    1. Li, Marui & Dong, Chaoyu & Xiong, Binyu & Mu, Yunfei & Yu, Xiaodan & Xiao, Qian & Jia, Hongjie, 2022. "STTEWS: A sequential-transformer thermal early warning system for lithium-ion battery safety," Applied Energy, Elsevier, vol. 328(C).
    2. Jie Deng & Chulheung Bae & James Marcicki & Alvaro Masias & Theodore Miller, 2018. "Safety modelling and testing of lithium-ion batteries in electrified vehicles," Nature Energy, Nature, vol. 3(4), pages 261-266, April.
    3. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    4. Hasnain Hafiz & Kosuke Suzuki & Bernardo Barbiellini & Naruki Tsuji & Naoaki Yabuuchi & Kentaro Yamamoto & Yuki Orikasa & Yoshiharu Uchimoto & Yoshiharu Sakurai & Hiroshi Sakurai & Arun Bansil & Venka, 2021. "Tomographic reconstruction of oxygen orbitals in lithium-rich battery materials," Nature, Nature, vol. 594(7862), pages 213-216, June.
    5. Peter M. Attia & Aditya Grover & Norman Jin & Kristen A. Severson & Todor M. Markov & Yang-Hung Liao & Michael H. Chen & Bryan Cheong & Nicholas Perkins & Zi Yang & Patrick K. Herring & Muratahan Ayko, 2020. "Closed-loop optimization of fast-charging protocols for batteries with machine learning," Nature, Nature, vol. 578(7795), pages 397-402, February.
    6. Janise McNair, 2022. "The 6G frequency switch that spares scientific services," Nature, Nature, vol. 606(7912), pages 34-35, June.
    7. Marwin H. S. Segler & Mike Preuss & Mark P. Waller, 2018. "Planning chemical syntheses with deep neural networks and symbolic AI," Nature, Nature, vol. 555(7698), pages 604-610, March.
    8. Dapai Shi & Jingyuan Zhao & Zhenghong Wang & Heng Zhao & Chika Eze & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health," Energies, MDPI, vol. 16(9), pages 1-19, April.
    9. Hong, Jichao & Wang, Zhenpo & Chen, Wen & Yao, Yongtao, 2019. "Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks," Applied Energy, Elsevier, vol. 254(C).
    10. 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.
    11. Sang-Hoon Park & Paul J. King & Ruiyuan Tian & Conor S. Boland & João Coelho & Chuanfang (John) Zhang & Patrick McBean & Niall McEvoy & Matthias P. Kremer & Dermot Daly & Jonathan N. Coleman & Valeria, 2019. "High areal capacity battery electrodes enabled by segregated nanotube networks," Nature Energy, Nature, vol. 4(7), pages 560-567, July.
    12. Yuanfeng Xu & Luis Elcoro & Zhi-Da Song & Benjamin J. Wieder & M. G. Vergniory & Nicolas Regnault & Yulin Chen & Claudia Felser & B. Andrei Bernevig, 2020. "High-throughput calculations of magnetic topological materials," Nature, Nature, vol. 586(7831), pages 702-707, October.
    13. Paul Raccuglia & Katherine C. Elbert & Philip D. F. Adler & Casey Falk & Malia B. Wenny & Aurelio Mollo & Matthias Zeller & Sorelle A. Friedler & Joshua Schrier & Alexander J. Norquist, 2016. "Machine-learning-assisted materials discovery using failed experiments," Nature, Nature, vol. 533(7601), pages 73-76, May.
    14. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    15. Marten Scheffer & Jordi Bascompte & William A. Brock & Victor Brovkin & Stephen R. Carpenter & Vasilis Dakos & Hermann Held & Egbert H. van Nes & Max Rietkerk & George Sugihara, 2009. "Early-warning signals for critical transitions," Nature, Nature, vol. 461(7260), pages 53-59, September.
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