Prediction of Electric Vehicle Transmission Efficiency Using a New Thermally Coupled Lubrication Model

Abstract

We present a new method to predict the power losses in electric vehicle (EV) transmission systems using a thermally coupled gearbox efficiency model. Friction losses in gear teeth contacts are predicted using an iterative procedure to account for the thermal coupling between the tooth temperature, oil viscosity, film thickness, friction, and oil rheology during a gear mesh cycle. Crucially, the prediction of the evolution of the coefficient of friction (COF) along the path of contact incorporates measured lubricant rheological parameters as well as measured boundary friction. This allows the model to differentiate between nominally similar lubricants in terms of their impact on EV transmission efficiency. Bearing and gear churning losses are predicted using existing empirical relationships. The effects of EV motor cooling and heat transfers in the heat exchanger on oil temperature are considered. Finally, heat transfer to the surroundings is accounted for so that the evolution of gearbox temperature over any given drive cycle can be predicted. The general approach presented here is applicable to any automotive gearbox while incorporating features specific to EVs. The model predictions are compared to real road