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Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health

Author

Listed:
  • Dapai Shi

    (Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441053, China
    These authors contributed equally to this work.)

  • Jingyuan Zhao

    (Institute of Transportation Studies, University of California-Davis, Davis, CA 95616, USA
    These authors contributed equally to this work.)

  • Zhenghong Wang

    (Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441053, China)

  • Heng Zhao

    (College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China)

  • Chika Eze

    (Department of Mechanical Engineering, University of California, Merced, CA 94720, USA)

  • Junbin Wang

    (BYD Automotive Engineering Research Institute, Shenzhen 518118, China)

  • Yubo Lian

    (BYD Automotive Engineering Research Institute, Shenzhen 518118, China)

  • Andrew F. Burke

    (Institute of Transportation Studies, University of California-Davis, Davis, CA 95616, USA)

Abstract

Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3855-:d:1137327
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    References listed on IDEAS

    as
    1. Nadia Drake, 2014. "Cloud computing beckons scientists," Nature, Nature, vol. 509(7502), pages 543-544, May.
    2. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures," Renewable Energy, Elsevier, vol. 198(C), pages 1328-1340.
    3. Babaeiyazdi, Iman & Rezaei-Zare, Afshin & Shokrzadeh, Shahab, 2021. "State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach," Energy, Elsevier, vol. 223(C).
    4. Maitane Berecibar, 2019. "Machine-learning techniques used to accurately predict battery life," Nature, Nature, vol. 568(7752), pages 325-326, April.
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    Cited by:

    1. 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).

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