IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v284y2023ics0360544223020935.html

Guidelines for designing a digital twin for Li-ion battery: A reference methodology

Author

Listed:
  • Semeraro, Concetta
  • Aljaghoub, Haya
  • Abdelkareem, Mohammad Ali
  • Alami, Abdul Hai
  • Dassisti, Michele
  • Olabi, A.G.

Abstract

The integration of digital technologies is causing a significant change in the energy sector. These innovations have transformed traditional energy grids into intelligent grids. As a result, the digital replica of Battery Energy Storage Systems (BESS) has become one of the most crucial components in the energy sector. Digital twin technology enables the seamless integration of BESS into intelligent grids and offers numerous benefits, such as easy identification and prediction of faults, real-time system monitoring, optimization, temperature regulation, and estimation of parameters. As a result, the overall performance of BESS is improved by the digital twin technology. Consequently, this paper discusses the general guidelines that must be followed to develop a digital twin for a Li-ion BESS successfully. The main function is to define how to design a digital twin able to optimize the system and facilitate early and predictive fault detection and diagnosis.

Suggested Citation

  • Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Dassisti, Michele & Olabi, A.G., 2023. "Guidelines for designing a digital twin for Li-ion battery: A reference methodology," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223020935
    DOI: 10.1016/j.energy.2023.128699
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223020935
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128699?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. Chen, Kang & Zhu, Xu & Anduv, Burkay & Jin, Xinqiao & Du, Zhimin, 2022. "Digital twins model and its updating method for heating, ventilation and air conditioning system using broad learning system algorithm," Energy, Elsevier, vol. 251(C).
    2. Huang, Yufeng & Tao, Jun & Sun, Gang & Wu, Tengyun & Yu, Liling & Zhao, Xinbin, 2023. "A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis," Energy, Elsevier, vol. 270(C).
    3. Sanjay Jain & Guodong Shao & Seung-Jun Shin, 2017. "Manufacturing data analytics using a virtual factory representation," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5450-5464, September.
    4. Bondarenko, Oleksiy & Fukuda, Tetsugo, 2020. "Development of a diesel engine’s digital twin for predicting propulsion system dynamics," Energy, Elsevier, vol. 196(C).
    5. Wei He & Michael Pecht & David Flynn & Fateme Dinmohammadi, 2018. "A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering," Energies, MDPI, vol. 11(8), pages 1-23, August.
    6. Eklas Hossain & Hossain Mansur Resalat Faruque & Md. Samiul Haque Sunny & Naeem Mohammad & Nafiu Nawar, 2020. "A Comprehensive Review on Energy Storage Systems: Types, Comparison, Current Scenario, Applications, Barriers, and Potential Solutions, Policies, and Future Prospects," Energies, MDPI, vol. 13(14), pages 1-127, July.
    7. Mahmood, Anzar & Javaid, Nadeem & Razzaq, Sohail, 2015. "A review of wireless communications for smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 248-260.
    8. Burston, Martin & Ranasinghe, Kavindu & Gardi, Alessandro & Parezanović, Vladimir & Ajaj, Rafic & Sabatini, Roberto, 2022. "Design principles and digital control of advanced distributed propulsion systems," Energy, Elsevier, vol. 241(C).
    9. Li, Hongcheng & Yang, Dan & Cao, Huajun & Ge, Weiwei & Chen, Erheng & Wen, Xuanhao & Li, Chongbo, 2022. "Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system," Energy, Elsevier, vol. 239(PC).
    10. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    11. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    12. Koo, Bonchan & Chang, Seungjoon & Kwon, Hweeung, 2023. "Digital twin for natural gas infrastructure operation and management via streaming dynamic mode decomposition with control," Energy, Elsevier, vol. 274(C).
    13. Kang, Yongzhe & Duan, Bin & Zhou, Zhongkai & Shang, Yunlong & Zhang, Chenghui, 2020. "Online multi-fault detection and diagnosis for battery packs in electric vehicles," Applied Energy, Elsevier, vol. 259(C).
    14. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
    15. Ziming Xu & Jun Xu & Zhechen Guo & Haitao Wang & Zheng Sun & Xuesong Mei, 2022. "Design and Optimization of a Novel Microchannel Battery Thermal Management System Based on Digital Twin," Energies, MDPI, vol. 15(4), pages 1-20, February.
    16. Lilia Tightiz & Hyosik Yang, 2020. "A Comprehensive Review on IoT Protocols’ Features in Smart Grid Communication," Energies, MDPI, vol. 13(11), pages 1-24, June.
    17. Chen, Ziyue & Huang, Lizhen, 2021. "Digital twins for information-sharing in remanufacturing supply chain: A review," Energy, Elsevier, vol. 220(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. Zhou, Xunli & Wang, Sirui & Hu, Zhenyu & Wang, Shijie & Liu, Xianchen & Zhang, Zhaosheng & Liu, Peng & Yu, Yongchao & Li, Lei, 2026. "Evolving safety challenges of power batteries in service: Insights and strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 229(C).
    2. Wang, Tianyi & Zhong, Wenqi & Chen, Xi & Zhou, Guanwen & Shi, Jianliang & Zhang, Baihua, 2025. "A digital twin of coal-fired boiler for physical fields prediction using flow-field-informed POD-XGBoost reduced-order modeling," Energy, Elsevier, vol. 329(C).
    3. Bendaouia, Ahmed & Wang, Fenfen & Ouifak, Hafsaa & Li, Jianzhi, 2026. "Digital twin for battery energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PC).

    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. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    2. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
    3. Cui, Zhipeng & Jing, Hao & Wang, Dengliang & Wang, Bo & Chen, Weixiong, 2025. "Hybrid modeling-based digital twin of the direct air cooling system for operational performance optimization," Energy, Elsevier, vol. 320(C).
    4. do Amaral, J.V.S. & dos Santos, C.H. & Montevechi, J.A.B. & de Queiroz, A.R., 2023. "Energy Digital Twin applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    5. Wang, Pengfei & Zhu, Ze & Liang, Wenlong & Liao, Longtao & Wan, Jiashuang, 2023. "Hybrid mechanistic and neural network modeling of nuclear reactors," Energy, Elsevier, vol. 282(C).
    6. Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.
    7. Huang Zhang & Zili Wang & Shuyou Zhang & Lemiao Qiu & Yang Wang & Feifan Xiang & Zhiwei Pan & Linhao Zhu & Jianrong Tan, 2025. "Digital-Triplet: a new three entities digital-twin paradigm for equipment fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 4895-4914, October.
    8. Huang, Yufeng & Sun, Gang & Rabczuk, Timon & Zhuang, Xiaoying, 2026. "Propagation and evolution graph method embedded with physical constraints for multi-factor coupled deep fault diagnosis in aero-engines," Energy, Elsevier, vol. 342(C).
    9. Chen, Fenglan & Chen, Xing, 2025. "Mitigating pollution emissions of manufacturing firms: Does input servitization matter? Empirical evidence from China," Energy, Elsevier, vol. 320(C).
    10. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    11. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    12. 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).
    13. Catrini, Pietro & La Villetta, M. & Kumar, Dhirendran Munith & Morale, Massimo & Piacentino, Antonio, 2024. "Analysis of the operation of air-cooled chillers with variable-speed fans for advanced energy-saving-oriented control strategies," Applied Energy, Elsevier, vol. 367(C).
    14. Ren, Song & Sun, Jing, 2024. "Multi-fault diagnosis strategy based on a non-redundant interleaved measurement circuit and improved fuzzy entropy for the battery system," Energy, Elsevier, vol. 292(C).
    15. Liu, Xinyu & Yang, Jianping & Yang, Chunhe & Zhang, Zheyuan & Chen, Weizhong, 2023. "Numerical simulation on cavern support of compressed air energy storage(CAES)considering thermo-mechanical coupling effect," Energy, Elsevier, vol. 282(C).
    16. Ponce, Pedro & Polasko, Kenneth & Molina, Arturo, 2016. "End user perceptions toward smart grid technology: Acceptance, adoption, risks, and trust," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 587-598.
    17. Boulif, Nadia & Evers, Renate & Driegen, Jelle & Nawaz, Ayesha & Borneman, Zandrie & Nijmeijer, Kitty, 2025. "The acid-base flow battery: Tradeoffs between energy density, efficiency, and stability," Applied Energy, Elsevier, vol. 383(C).
    18. Jing Hou & He He & Yan Yang & Tian Gao & Yifan Zhang, 2019. "A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(9), pages 1-23, May.
    19. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    20. Yao Ahoutou & Adrian Ilinca & Mohamad Issa, 2022. "Electrochemical Cells and Storage Technologies to Increase Renewable Energy Share in Cold Climate Conditions—A Critical Assessment," Energies, MDPI, vol. 15(4), pages 1-30, February.

    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:284:y:2023:i:c:s0360544223020935. 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.