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Dual-Metric-Driven Thermal–Fluid Coupling Modeling and Thermal Management Optimization for High-Speed Electric Multiple Unit Electrical Cabinets

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  • Yaxuan Wang

    (School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China)

  • Cuifeng Xu

    (School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China)

  • Shushen Chen

    (School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China)

  • Ziyi Deng

    (School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China)

  • Zijun Teng

    (School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China)

Abstract

To address thermal management challenges in CR400BF high-speed EMU electrical cabinets—stemming from heterogeneous component integration, multi-condition dynamic thermal loads, and topological configuration variations—a dual-metric-driven finite element model calibration method is proposed using ANSYS Workbench. A multi-objective optimization function, constructed via the coefficient of determination ( R 2 ) and root mean square error ( R M S E ), integrates gradient descent to inversely solve key parameters, achieving precise global–local model matching. This establishes an equivalent model library of 52 components, enabling rapid development of multi-physical-field coupling models for electrical cabinets via parameterization and modularization. The framework supports temperature field analysis, thermal fault prediction, and optimization design for multi-topology cabinets under diverse operating conditions. Validation via simulations and real-vehicle tests demonstrates an average temperature prediction error ≤ 10 % , verifying reliability. A thermal management optimization scheme is further developed, constructing a full-process technical framework spanning model calibration to control for electrical cabinet thermal design. This advances precision thermal management in rail transit systems, enhancing equipment safety and energy efficiency while providing a scalable engineering solution for high-speed train thermal design.

Suggested Citation

  • Yaxuan Wang & Cuifeng Xu & Shushen Chen & Ziyi Deng & Zijun Teng, 2025. "Dual-Metric-Driven Thermal–Fluid Coupling Modeling and Thermal Management Optimization for High-Speed Electric Multiple Unit Electrical Cabinets," Energies, MDPI, vol. 18(17), pages 1-23, September.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4693-:d:1741873
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    References listed on IDEAS

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    1. Yaseen Ahmed Mohammed Alsumaidaee & Chong Tak Yaw & Siaw Paw Koh & Sieh Kiong Tiong & Chai Phing Chen & Kharudin Ali, 2022. "Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning," Energies, MDPI, vol. 15(18), pages 1-34, September.
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