Abstract
This paper presents the heat transfer analysis of the electric vehicle motor and optimization of an operating parameter for improving the performance of the motor with an oil cooling system. The homogeneous multi-phase flow model in commercial software, ANSYS CFX, is applied to solve the flow of mixture, including oil and air. We mainly focus on the influence of operating (oil level) and driving conditions (roll and pitch angles) on the cooling system. Several CFD simulations are performed with various input parameters (oil level, roll, and pitch angles) generated from the Latin Hypercube Sampling method. Based on the CFD results, we built a Polynomial Chaos Expansions based surrogate model to predict the temperature distribution, maximum temperature, and torque loss, which respond to the input parameters. Sensitivity analysis indicates that the oil level significantly affects system performance (maximum temperature and torque loss). Finally, the optimal range of oil level is estimated, considering the maximum allowable temperature of the motor.
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