IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i9p3855-d1137327.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/9/3855/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/9/3855/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nadia Drake, 2014. "Cloud computing beckons scientists," Nature, Nature, vol. 509(7502), pages 543-544, May.
    2. Maitane Berecibar, 2019. "Machine-learning techniques used to accurately predict battery life," Nature, Nature, vol. 568(7752), pages 325-326, April.
    3. 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.
    4. 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).
    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. 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).

    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. Dapai Shi & Jingyuan Zhao & Chika Eze & Zhenghong Wang & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Artificial Intelligence Framework for Battery Management System," Energies, MDPI, vol. 16(11), pages 1-21, May.
    2. 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.
    3. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    4. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    5. Xiong, Wei & Xie, Fang & Xu, Gang & Li, Yumei & Li, Ben & Mo, Yimin & Ma, Fei & Wei, Keke, 2023. "Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope," Renewable Energy, Elsevier, vol. 218(C).
    6. Yanling Zheng & Liping Zhang & XiXun Zhu & Gang Guo, 2020. "A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-12, June.
    7. He, Rong & He, Yongling & Xie, Wenlong & Guo, Bin & Yang, Shichun, 2023. "Comparative analysis for commercial li-ion batteries degradation using the distribution of relaxation time method based on electrochemical impedance spectroscopy," Energy, Elsevier, vol. 263(PD).
    8. Calise, Francesco & Cappiello, Francesco Liberato & Cimmino, Luca & Dentice d’Accadia, Massimo & Vicidomini, Maria, 2023. "Renewable smart energy network: A thermoeconomic comparison between conventional lithium-ion batteries and reversible solid oxide fuel cells," Renewable Energy, Elsevier, vol. 214(C), pages 74-95.
    9. Zizhen Cheng & Li Wang & Yumeng Yang, 2023. "A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting," Energies, MDPI, vol. 16(7), pages 1-18, March.
    10. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    11. Lijun Zhu & Jian Wang & Yutao Wang & Bin Pan & Lujun Wang, 2024. "Detection of Impedance Inhomogeneity in Lithium-Ion Battery Packs Based on Local Outlier Factor," Energies, MDPI, vol. 17(20), pages 1-20, October.
    12. Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.
    13. Shaotong Qi & Yubo Cheng & Zhiyuan Li & Jiaxin Wang & Huaiyi Li & Chunwei Zhang, 2024. "Advanced Deep Learning Techniques for Battery Thermal Management in New Energy Vehicles," Energies, MDPI, vol. 17(16), pages 1-38, August.
    14. Fujin Wang & Zhi Zhai & Zhibin Zhao & Yi Di & Xuefeng Chen, 2024. "Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    15. Zhihang Zhang & Languang Lu & Yalun Li & Hewu Wang & Minggao Ouyang, 2023. "Accurate Remaining Available Energy Estimation of LiFePO 4 Battery in Dynamic Frequency Regulation for EVs with Thermal-Electric-Hysteresis Model," Energies, MDPI, vol. 16(13), pages 1-28, July.
    16. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
    17. Chen, Xiang & Deng, Yelin & Wang, Xingxing & Yuan, Yinnan, 2024. "The capacity degradation path prediction for the prismatic lithium-ion batteries based on the multi-features extraction with SGPR," Energy, Elsevier, vol. 299(C).
    18. Li, Renzheng & Wang, Hui & Dai, Haifeng & Hong, Jichao & Tong, Guangyao & Chen, Xinbo, 2022. "Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network," Energy, Elsevier, vol. 250(C).
    19. Francesco Calise & Francesco Liberato Cappiello & Luca Cimmino & Massimo Dentice d’Accadia & Maria Vicidomini, 2024. "A Novel Layout for Combined Heat and Power Production for a Hospital Based on a Solid Oxide Fuel Cell," Energies, MDPI, vol. 17(5), pages 1-21, February.
    20. Julan Chen & Guangheng Qi & Kai Wang, 2023. "Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review," Energies, MDPI, vol. 16(17), pages 1-22, August.

    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:16:y:2023:i:9:p:3855-:d:1137327. 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.