Analytical modelling technique of oil-cooled electric machine using empirical correlation and LPTN

The demand of high power dense machines has pushed machine performance to the limit. This leads to the challenges of keeping the temperature of active components below their thermal limits to ensure machine safety and reliability. For automotive applications, oil cooling is well-known to be an effective cooling method when compared to water cooling, which has been used for a decade. This paper proposes a computationally efficient thermal modelling technique that enables fast and accurate evaluation of machine performance with oil cooling based on Lumped Parameter Thermal Network (LPTN). The convective heat transfer coefficients that used to model the oil cooling effect are computed from empirical correlations developed specifically for electric machines for automotive applications. The proposed solution enables thermal model to be considered in multiphysics design and optimization of an electric machine, drive cycle performance analysis, and electric drive unit system design and optimization.