IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v331y2025ics0360544225026581.html

AI-assisted digital twin modeling technology for lithium-ion batteries

Author

Listed:
  • Liu, Meiwen
  • Wang, Yaxuan
  • Li, Junfu
  • Yang, Ke
  • Guo, Shilong
  • Zhao, Lei
  • Wang, Zhenbo

Abstract

Accurate assessment of state of health (SOH) for lithium-ion batteries throughout their entire lifecycle and real-time voltage tracking prediction have become urgent requirement to ensure battery system safety and reliability. This paper develops a cloud-edge collaborative digital twin architecture to estimate SOH for LiCoO2 cells and predict the real-time voltage. First, a long short-term memory (LSTM) network model is adopted in the cloud side, where health features are extracted from the current segments of the constant voltage charging phase to estimate the cell's SOH. Subsequently, based on the estimated SOH, the cloud side matches electrochemical parameters and transmits them to the edge-side battery management system (BMS), triggering the rapid simulation by an improved single-particle (SP+) model to obtain preliminary voltage predictions. Considering the impact of cell-to-cell variations and temperature fluctuations on voltage simulation accuracy, an online correction model based on a fully connected neural network (F-CNN) is further constructed to refine the simulation output, ultimately yielding high-accuracy voltage predictions. Experimental results demonstrate that the developed digital twin model enables precise SOH estimation throughout the entire cell lifecycle and achieves high-precision real-time voltage prediction, providing robust support for battery health management and safety applications.

Suggested Citation

  • Liu, Meiwen & Wang, Yaxuan & Li, Junfu & Yang, Ke & Guo, Shilong & Zhao, Lei & Wang, Zhenbo, 2025. "AI-assisted digital twin modeling technology for lithium-ion batteries," Energy, Elsevier, vol. 331(C).
    • Handle: RePEc:eee:energy:v:331:y:2025:i:c:s0360544225026581
    DOI: 10.1016/j.energy.2025.137016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225026581
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.137016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Li, Tianfu & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Bhatti, Ghanishtha & Mohan, Harshit & Raja Singh, R., 2021. "Towards the future of smart electric vehicles: Digital twin technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    4. Lin, Chuanping & Xu, Jun & Shi, Mingjie & Mei, Xuesong, 2022. "Constant current charging time based fast state-of-health estimation for lithium-ion batteries," Energy, Elsevier, vol. 247(C).
    5. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
    6. Peng, Simin & Wang, Yujian & Tang, Aihua & Jiang, Yuxia & Kan, Jiarong & Pecht, Michael, 2025. "State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries," Energy, Elsevier, vol. 315(C).
    7. 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).
    8. Chen, Junxiong & Hu, Yuanjiang & Zhu, Qiao & Rashid, Haroon & Li, Hongkun, 2023. "A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging," Energy, Elsevier, vol. 282(C).
    9. García, Antonio & Monsalve-Serrano, Javier & Ponce-Mora, Alberto & Fogué-Robles, Álvaro, 2023. "Development of a calibration methodology for fitting the response of a lithium-ion cell P2D model using real driving cycles," Energy, Elsevier, vol. 271(C).
    10. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
    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. Yang, Simin & Zhou, Jiahua & Chen, Binbin & An, Ruifeng & Zhao, Ziyu & Fan, Yuqian & Guan, Quanxue & Tan, Xiaojun, 2025. "Deep domain adaptation for cross-chemistry battery SOH prediction with relaxation voltage features," Energy, Elsevier, vol. 339(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. Zhang, Shuxin & Liu, Zhitao & Su, Hongye, 2023. "State of health estimation for lithium-ion batteries on few-shot learning," Energy, Elsevier, vol. 268(C).
    2. Chen, Zhang & Shen, Wenjing & Chen, Liqun & Wang, Shuqiang, 2022. "Adaptive online capacity prediction based on transfer learning for fast charging lithium-ion batteries," Energy, Elsevier, vol. 248(C).
    3. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    4. Kim, Sung Wook & Oh, Ki-Yong & Lee, Seungchul, 2022. "Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries," Applied Energy, Elsevier, vol. 315(C).
    5. Li, Xiaopeng & Zhao, Minghang & Zhong, Shisheng & Li, Junfu & Fu, Song & Yan, Zhiqi, 2024. "BMSFormer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early SOC data with strong correlated single health indicator," Energy, Elsevier, vol. 313(C).
    6. Zhao, Jingyuan & Wang, Zhenghong & Wu, Yuyan & Burke, Andrew F., 2025. "Predictive pretrained transformer (PPT) for real-time battery health diagnostics," Applied Energy, Elsevier, vol. 377(PD).
    7. 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).
    8. Huang, Zhelin & Xu, Fan & Yang, Fangfang, 2023. "State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model," Energy, Elsevier, vol. 262(PB).
    9. Chen, Jianguo & Wang, Yu & Guo, Dongxu & Shen, Yifan & Sun, Tao & Han, Xuebing & Zheng, Yuejiu & Ouyang, Minggao, 2025. "Deep learning model for remaining useful life prediction with reduced labeling data dependency," Applied Energy, Elsevier, vol. 402(PA).
    10. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    11. Yao, Xing-Yan & Chen, Liwei, 2025. "Battery state of health estimation with interpretable distance feature and dynamic weight model across-chemistry and working conditions," Energy, Elsevier, vol. 332(C).
    12. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    13. Liu, Ruixue & Jiang, Benben, 2025. "A multi-time-resolution attention-based interaction network for co-estimation of multiple battery states," Applied Energy, Elsevier, vol. 381(C).
    14. Li, Yang & Gao, Guoqiang & Chen, Kui & He, Shuhang & Liu, Kai & Xin, Dongli & Luo, Yang & Long, Zhou & Wu, Guangning, 2025. "State-of-health prediction of lithium-ion batteries using feature fusion and a hybrid neural network model," Energy, Elsevier, vol. 319(C).
    15. Yan, Lisen & Peng, Jun & Gao, Dianzhu & Wu, Yue & Liu, Yongjie & Li, Heng & Liu, Weirong & Huang, Zhiwu, 2022. "A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery," Energy, Elsevier, vol. 243(C).
    16. Chen, Kui & Luo, Yang & Long, Zhou & Li, Yang & Nie, Guangbo & Liu, Kai & Xin, Dongli & Gao, Guoqiang & Wu, Guangning, 2025. "Big data-driven prognostics and health management of lithium-ion batteries:A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
    17. Sonthalia, Ankit & Femilda Josephin, J.S. & Varuvel, Edwin Geo & Chinnathambi, Arunachalam & Subramanian, Thiyagarajan & Kiani, Farzad, 2025. "A deep learning multi-feature based fusion model for predicting the state of health of lithium-ion batteries," Energy, Elsevier, vol. 317(C).
    18. Ruan, Haokai & Wei, Zhongbao & Shang, Wentao & Wang, Xuechao & He, Hongwen, 2023. "Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging," Applied Energy, Elsevier, vol. 336(C).
    19. Wang, Zhen & Zhao, Li & Li, Yiding & Wang, Wenwei, 2025. "A data-efficient method for lithium-ion battery state-of-health estimation based on real-time frequent itemset image encoding," Applied Energy, Elsevier, vol. 398(C).
    20. Chen, Si-Zhe & Liu, Jing & Yuan, Haoliang & Tao, Yibin & Xu, Fangyuan & Yang, Ling, 2025. "AM-MFF: A multi-feature fusion framework based on attention mechanism for robust and interpretable lithium-ion battery state of health estimation," Applied Energy, Elsevier, vol. 381(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:energy:v:331:y:2025:i:c:s0360544225026581. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.