IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v179y2023ics1364032123001363.html
   My bibliography  Save this article

Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms

Author

Listed:
  • Naseri, F.
  • Gil, S.
  • Barbu, C.
  • Cetkin, E.
  • Yarimca, G.
  • Jensen, A.C.
  • Larsen, P.G.
  • Gomes, C.

Abstract

Transportation electrification has been fueled by recent advancements in the technology and manufacturing of battery systems, but the industry yet is facing serious challenges that could be addressed using cutting-edge digital technologies. One such novel technology is based on the digital twining of battery systems. Digital twins (DTs) of batteries utilize advanced multi-layer models, artificial intelligence, advanced sensing units, Internet-of-Things technologies, and cloud computing techniques to provide a virtual live representation of the real battery system (the physical twin) to improve the performance, safety, and cost-effectiveness. Furthermore, they orchestrate the operation of the entire battery value chain offering great advantages, such as improving the economy of manufacturing, re-purposing, and recycling processes. In this context, various studies have been carried out discussing the DT applications and use cases from cloud-enabled battery management systems to the digitalization of battery testing. This work provides a comprehensive review of different possible use cases, key enabling technologies, and requirements for battery DTs. The review inclusively discusses the use cases, development/integration platforms, as well as hardware and software requirements for implementation of the battery DTs, including electrical topics related to the modeling and algorithmic approaches, software architectures, and digital platforms for DT development and integration. The existing challenges are identified and circumstances that will create enough value to justify these challenges, such as the added costs, are discussed.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:179:y:2023:i:c:s1364032123001363
    DOI: 10.1016/j.rser.2023.113280
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032123001363
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2023.113280?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Yu, Jianxi & Liu, Pei & Li, Zheng, 2020. "Hybrid modelling and digital twin development of a steam turbine control stage for online performance monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    2. Yu, Wei & Patros, Panos & Young, Brent & Klinac, Elsa & Walmsley, Timothy Gordon, 2022. "Energy digital twin technology for industrial energy management: Classification, challenges and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    3. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    4. Di Silvestre, Maria Luisa & Favuzza, Salvatore & Riva Sanseverino, Eleonora & Zizzo, Gaetano, 2018. "How Decarbonization, Digitalization and Decentralization are changing key power infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 483-498.
    5. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    6. Held, Marcel & Tuchschmid, Martin & Zennegg, Markus & Figi, Renato & Schreiner, Claudia & Mellert, Lars Derek & Welte, Urs & Kompatscher, Michael & Hermann, Michael & Nachef, Léa, 2022. "Thermal runaway and fire of electric vehicle lithium-ion battery and contamination of infrastructure facility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    7. Li, Weihan & Cui, Han & Nemeth, Thomas & Jansen, Jonathan & Ünlübayir, Cem & Wei, Zhongbao & Feng, Xuning & Han, Xuebing & Ouyang, Minggao & Dai, Haifeng & Wei, Xuezhe & Sauer, Dirk Uwe, 2021. "Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning," Applied Energy, Elsevier, vol. 293(C).
    8. Leng, Jiewu & Ruan, Guolei & Jiang, Pingyu & Xu, Kailin & Liu, Qiang & Zhou, Xueliang & Liu, Chao, 2020. "Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    9. Jasiūnas, Justinas & Lund, Peter D. & Mikkola, Jani, 2021. "Energy system resilience – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    10. Moreira, Naiara & Molina, Elías & Lázaro, Jesús & Jacob, Eduardo & Astarloa, Armando, 2016. "Cyber-security in substation automation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1552-1562.
    11. Tao, Laifa & Ma, Jian & Cheng, Yujie & Noktehdan, Azadeh & Chong, Jin & Lu, Chen, 2017. "A review of stochastic battery models and health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 716-732.
    12. Mousavi G., S.M. & Nikdel, M., 2014. "Various battery models for various simulation studies and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 477-485.
    13. Yoro, Kelvin O. & Daramola, Michael O. & Sekoai, Patrick T. & Wilson, Uwemedimo N. & Eterigho-Ikelegbe, Orevaoghene, 2021. "Update on current approaches, challenges, and prospects of modeling and simulation in renewable and sustainable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    14. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    15. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    16. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    17. Naseri, F. & Karimi, S. & Farjah, E. & Schaltz, E., 2022. "Supercapacitor management system: A comprehensive review of modeling, estimation, balancing, and protection techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    18. 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).
    19. 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.
    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. Ama Ranawaka & Damminda Alahakoon & Yuan Sun & Kushan Hewapathirana, 2024. "Leveraging the Synergy of Digital Twins and Artificial Intelligence for Sustainable Power Grids: A Scoping Review," Energies, MDPI, vol. 17(21), pages 1-52, October.

    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. 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).
    2. Jiang, Bo & Tao, Siyi & Wang, Xueyuan & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique," Energy, Elsevier, vol. 278(PA).
    3. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    4. Lai, Xin & Huang, Yunfeng & Gu, Huanghui & Han, Xuebing & Feng, Xuning & Dai, Haifeng & Zheng, Yuejiu & Ouyang, Minggao, 2022. "Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects," Energy, Elsevier, vol. 238(PA).
    5. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    6. 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).
    7. Roman Gozdur & Tomasz Przerywacz & Dariusz Bogdański, 2021. "Low Power Modular Battery Management System with a Wireless Communication Interface," Energies, MDPI, vol. 14(19), pages 1-20, October.
    8. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    9. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    10. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    11. Guo, Yongfang & Yu, Xiangyuan & Wang, Yashuang & Huang, Kai, 2024. "Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    12. Tian, Jiaqiang & Fan, Yuan & Pan, Tianhong & Zhang, Xu & Yin, Jianning & Zhang, Qingping, 2024. "A critical review on inconsistency mechanism, evaluation methods and improvement measures for lithium-ion battery energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    13. Wu, Muyao & Zhong, Yiming & Wu, Ji & Wang, Yuqing & Wang, Li, 2023. "State of health estimation of the lithium-ion power battery based on the principal component analysis-particle swarm optimization-back propagation neural network," Energy, Elsevier, vol. 283(C).
    14. Yang, Bowen & Wang, Dafang & Yu, Beike & Wang, Facheng & Chen, Shiqin & Sun, Xu & Dong, Haosong, 2024. "Research on online passive electrochemical impedance spectroscopy and its outlook in battery management," Applied Energy, Elsevier, vol. 363(C).
    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. Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
    18. Kuang, Pan & Zhou, Fei & Xu, Shuai & Li, Kangqun & Xu, Xiaobin, 2024. "State-of-charge estimation hybrid method for lithium-ion batteries using BiGRU and AM co-modified Seq2Seq network and H-infinity filter," Energy, Elsevier, vol. 300(C).
    19. Oskar Juszczyk & Khuram Shahzad, 2022. "Blockchain Technology for Renewable Energy: Principles, Applications and Prospects," Energies, MDPI, vol. 15(13), pages 1-24, June.
    20. Hou, Jie & Liu, Jiawei & Chen, Fengwei & Li, Penghua & Zhang, Tao & Jiang, Jincheng & Chen, Xiaolei, 2023. "Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter," Energy, Elsevier, vol. 271(C).

    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:rensus:v:179:y:2023:i:c:s1364032123001363. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.