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Design and Optimization of a Novel Microchannel Battery Thermal Management System Based on Digital Twin

Author

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  • Ziming Xu

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
    Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
    These authors contributed equally to this work.)

  • Jun Xu

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
    Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
    These authors contributed equally to this work.)

  • Zhechen Guo

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
    Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Haitao Wang

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
    Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Zheng Sun

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
    Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Xuesong Mei

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
    Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

In order to avoid high-temperature and large rate discharge impact on the performance of battery modules, a microchannel liquid cooling battery thermal management system (BTMS) and BTMS virtual model of the microchannel structure based on digital twin (DT) is proposed. On the basis of accurate virtual simulation model, the computational fluid dynamics (CFD) model and the Gaussian process regression algorithm were combined to drive the optimization process in order to improve the cooling capacity of the system. The results show that the microchannel plates can greatly enhance the cooling capacity of the direct cooling system and effectively improve the uniformity of the coolant. The width of the microchannel plates and the side spacing actually represent the amount of coolant flowing through the inside and outside of the battery module, which significantly impacts the maximum temperature and maximum temperature difference. Increasing the coolant flow can only effectively improve the cooling capacity of the module to a limited extent. Gaussian process regression based on the DT virtual model is more suitable for analyzing the interaction between multiple factors and obtaining global optimization results. After optimization, the maximum temperature and the maximum temperature difference of the system are reduced by 4.02 °C and 5.05 °C, respectively. The proposed structure and method are expected to provide insights into the design and development of battery thermal management systems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1421-:d:750186
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    References listed on IDEAS

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    Cited by:

    1. Junhao Dong & Xipo Lu & Yang Sun & Vladislav Mitin & Huaping Xu & Wei Kong, 2022. "Design of Battery Thermal Management System with Considering the Longitudinal and Transverse Temperature Difference," Energies, MDPI, vol. 15(19), pages 1-13, October.
    2. 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).
    3. Szabolcs Kocsis Szürke & Mátyás Szabó & Szabolcs Szalai & Szabolcs Fischer, 2024. "Deformation Analysis of Different Lithium Battery Designs Using the DIC Technique," Energies, MDPI, vol. 17(2), pages 1-23, January.
    4. 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).
    5. 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.
    6. 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).

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