Optimization of Electric Vehicle Drivetrain Fluid with a New System-Level Approach.

This paper uses a newly developed tribology-based system-level transmission efficiency model to investigate the influence of e-fluid properties on electric vehicle (EV) drivetrain losses. The model considers gear meshing losses using a thermally-coupled mixed friction prediction, bearing losses using existing models, and gear churning using a new experimentally-derived regression equation. The key advantages of the approach are: (i) it is a system-level approach that accounts for the interdependency of different sources of losses by predicting the evolution of temperature distribution in the entire electric drive unit (EDU) including the transmission, e-motor and heat exchanger; (ii) it can discriminate between two oils of the same specification in terms of their impact on overall losses by using measured lubricant rheology; and (iii) it predicts total energy loss over any vehicle duty cycle. The model is validated by comparing its temperature predictions to in-situ measurements made on a real EV in a series of road tests. Application of the model to a typical modern EV shows that it is possible to identify an optimum e-fluid viscosity for minimum transmission losses over any given drive cycle. The exact value of this optimum strongly depends on vehicle duty: it is higher for a city cycle such as the New York City Cycle (NYCC), which has low average speed and frequent start-stops, conditions where gear tooth friction is shown to dominate, and lower for highway driving or the worldwide harmonized light-duty vehicles test cycle (WLTC), where bearing losses dominate. The presented approach provides an efficient tool for optimization of lubricant selection and EDU design.