IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v273y2023ics0360544223004802.html
   My bibliography  Save this article

Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining

Author

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

Abstract

Energy sector is being revolutionized with the introduction of digitalization technologies. Digitalization technologies converted conventional energy grids into smart grids. Therefore, the virtual representation of battery energy storage systems, known as a digital twin, has become a highly valuable tool in the energy industry. This technology seamlessly integrates battery energy storage systems into smart grids and facilitates fault detection and prognosis, real-time monitoring, temperature control, optimization, and parameter estimations. In general, the use of digital twin technology improves the efficiency of the battery system after a thorough assessment of the battery performance. Hence, this paper aims to review the advancements of digital twin technology in battery energy storage systems. In particular, this paper focuses on the different functions and architectures of the digital twin for battery energy storage systems. Then, this paper further analyzes the digital twin characteristics using the Formal Concept Analysis (FCA) algorithm. The FCA is run to find trends and gaps between the digital twin functions and architectures in the battery system. Exploring the trends and gaps from previous research associated with the integration of digital twin with battery energy systems is essential to pave the way for further enhancements in this field.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:273:y:2023:i:c:s0360544223004802
    DOI: 10.1016/j.energy.2023.127086
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223004802
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127086?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. Tian, Xi & Gong, Yu & Wu, Yufeng & Agyeiwaa, Amma & Zuo, Tieyong, 2014. "Management of used lead acid battery in China: Secondary lead industry progress, policies and problems," Resources, Conservation & Recycling, Elsevier, vol. 93(C), pages 75-84.
    2. 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).
    3. He, Wenbin & Mao, Jianxu & Song, Kai & Li, Zhe & Su, Yulong & Wang, Yaonan & Pan, Xiangcheng, 2023. "Structural performance prediction based on the digital twin model: A battery bracket example," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Olabi, A.G. & Wilberforce, Tabbi & Sayed, Enas Taha & Abo-Khalil, Ahmed G. & Maghrabie, Hussein M. & Elsaid, Khaled & Abdelkareem, Mohammad Ali, 2022. "Battery energy storage systems and SWOT (strengths, weakness, opportunities, and threats) analysis of batteries in power transmission," Energy, Elsevier, vol. 254(PA).
    5. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    6. Mali, Vima & Saxena, Rajat & Kumar, Kundan & Kalam, Abul & Tripathi, Brijesh, 2021. "Review on battery thermal management systems for energy-efficient electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    7. 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).
    8. Hyang-A Park & Gilsung Byeon & Wanbin Son & Hyung-Chul Jo & Jongyul Kim & Sungshin Kim, 2020. "Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin," Energies, MDPI, vol. 13(20), pages 1-15, October.
    9. Chen, Ziyue & Huang, Lizhen, 2021. "Digital twins for information-sharing in remanufacturing supply chain: A review," Energy, Elsevier, vol. 220(C).
    10. 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).
    11. Bondarenko, Oleksiy & Fukuda, Tetsugo, 2020. "Development of a diesel engine’s digital twin for predicting propulsion system dynamics," Energy, Elsevier, vol. 196(C).
    12. 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).
    13. Qiang Li & Tanghu Zhang & Tianyu Zhang & Zhichao Xue & Hong Sun, 2022. "Study on Two-Phase Permeation of Oxygen and Electrolyte in Lithium Air Battery Electrode Based on Digital Twin," Energies, MDPI, vol. 15(19), pages 1-12, September.
    14. 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).
    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.
    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. Diyono Diyono & Hans Cappon & Katarzyna Kujawa-Roeleveld & Karel J. Keesman, 2023. "Designing Sustainable Domestic Electricity Supply from Renewable Energy Mixes: Application to Java and Bali, Indonesia," Energies, MDPI, vol. 16(22), pages 1-19, November.
    2. 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).
    3. Huang, Chu & Zhu, Haixi & Ma, Yinjie & E, Jiaqiang, 2023. "Evaluation of lithium battery immersion thermal management using a novel pentaerythritol ester coolant," Energy, Elsevier, vol. 284(C).
    4. 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).
    5. 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).

    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 & Dassisti, Michele & Olabi, A.G., 2023. "Guidelines for designing a digital twin for Li-ion battery: A reference methodology," Energy, Elsevier, vol. 284(C).
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. 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).
    7. Sun, Shulei & Ma, Chunyu & Wang, Xiyu & Yang, Ye & Mei, Jun, 2024. "Design and optimisation of a novel serpentine flow channel with branch structure," Energy, Elsevier, vol. 293(C).
    8. Sifat, Md. Mhamud Hussen & Choudhury, Safwat Mukarrama & Das, Sajal K. & Pota, Hemanshu & Yang, Fuwen, 2025. "Novel abstractions and experimental validation for digital twin microgrid design: Lab scale studies and large scale proposals," Applied Energy, Elsevier, vol. 377(PC).
    9. 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).
    10. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    11. Wu, Chunxia & Sun, Yalong & Tang, Heng & Zhang, Shiwei & Yuan, Wei & Zhu, Likuan & Tang, Yong, 2024. "A review on the liquid cooling thermal management system of lithium-ion batteries," Applied Energy, Elsevier, vol. 375(C).
    12. Zhanbin Luo & Jing Ma & Fu Chen & Xiaoxiao Li & Shaoliang Zhang, 2018. "Effects of Pb Smelting on the Soil Bacterial Community near a Secondary Lead Plant," IJERPH, MDPI, vol. 15(5), pages 1-16, May.
    13. Balakumar Balasingam & Mostafa Ahmed & Krishna Pattipati, 2020. "Battery Management Systems—Challenges and Some Solutions," Energies, MDPI, vol. 13(11), pages 1-19, June.
    14. Yuan, Sai & Zhou, Ran & Li, Mengna & Lv, Chengchao, 2023. "Investigating the influence of digital technology application on employee compensation," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    15. Gharehghani, Ayat & Rabiei, Moeed & Mehranfar, Sadegh & Saeedipour, Soheil & Mahmoudzadeh Andwari, Amin & García, Antonio & Reche, Carlos Mico, 2024. "Progress in battery thermal management systems technologies for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    16. Ahmed G. Abo-Khalil & Mohammad Alobaid, 2023. "A Guide to the Integration and Utilization of Energy Storage Systems with a Focus on Demand Resource Management and Power Quality Enhancement," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    17. Xu, Jing & Cui, Zhipeng & Ma, Suxia & Wang, Xiaowei & Zhang, Zhiyao & Zhang, Guoxia, 2024. "Data based digital twin for operational performance optimization in CFB boilers," Energy, Elsevier, vol. 306(C).
    18. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    19. Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    20. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Meng, Dean, 2023. "Online diagnosis and prediction of power battery voltage comprehensive faults for electric vehicles based on multi-parameter characterization and improved K-means method," Energy, Elsevier, vol. 283(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:energy:v:273:y:2023:i:c:s0360544223004802. 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.