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Faculty of Graduate Studies

Chon Him Wong

  • BEng (University of Victoria, 2021)

Notice of the Final Oral Examination for the Degree of Master of Applied Science

Topic

Optimizing Electric Vehicle Thermal Management System Using a CFD Simulation-derived GPR-ANN Metamodels

Department of Mechanical Engineering

Date & location

  • Monday, April 20, 2026
  • 4:30 P.M.
  • Engineering Office Wing
  • Room 502 and Virtual

Reviewers

Supervisory Committee

  • Dr. Zuomin Dong, Department of Mechanical Engineering, University of Victoria (Supervisor)

  • Dr. Keivan Ahmadi, Department of Mechanical Engineering, UVic (Member) 

External Examiner

  • Dr. Han-chuan Yang, Department of Electrical and Computer Engineering, University of Victoria 

Chair of Oral Examination

  • Dr. Peter Dietsch, Department of Philosophy, UVic

     

Abstract

Transportation is a major contributor to global greenhouse gas (GHG) emissions. Battery electric vehicles (BEVs) have gained widespread adoption in passenger car applications due to their high energy efficiency and potential to reduce carbon emissions. Extending BEV technology to medium-duty trucks (MDTs), which are widely used in commercial transportation, can further improve energy efficiency and reduce GHG emissions. 

The thermal management system (TMS) for the battery energy storage system (BESS), propulsion motors, and power electronics is essential to ensure safe, reliable, and efficient BEV operation while extending battery life. For electric medium-duty trucks (e-MDTs), which operate under diverse and demanding duty cycles, designing an effective TMS is particularly challenging due to the complex heat transfer processes within battery packs and their associated liquid-cooling and heating systems. 

High-fidelity computational fluid dynamics (CFD) simulations are typically required to evaluate and optimize BESS thermal management performance. However, such simulations are computationally intensive, making large-scale design exploration across varying operating conditions impractical. 

This work develops data-driven Gaussian Process Regression (GPR) and Artificial Neural Network (ANN) metamodels to approximate the thermal behaviour of BESS systems and enable efficient BTMS design optimization. A numerical TMS model for an e-MDT is developed in MATLAB/Simulink, and CFD simulations are conducted for multiple battery pack and BTMS configurations under representative driving conditions. The resulting simulation data are used to train GPR-ANN metamodels that predict cooling performance without requiring repeated CFD simulations. 

The proposed framework reduces simulation time by approximately 97% while maintaining a maximum prediction error of 8%, providing an efficient and accurate approach for the model-based design and optimization of BTMS for e-MDTs.

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