The implementation of increasingly stricter regulations on CO2 emissions by the European Community is pushing the automotive industry towards a radical change. In a rush to electrify their model ranges, global carmakers are investing heavily on developing new electrified powertrains.
Within this context, this work focuses on the analysis of electric axles drives (eAxles) for a BEV (battery electric vehicle) sport car, with the aim to develop an analytical tool useful to perform predictive analysis in the concept design phase.
Through a parametric definition of the procedure, the tool is able to “adapt” to any drivetrain layout analysed. The tool actually allows to enter more than 100 input values including lubrication conditions (oil viscosity and operating temperature), gears (number, macrogeometry, mesh), bearings (number, type, geometry, mounting layout, angle mesh), shafts, oil seals, external layout and external fluid conditions.
The tool allows to calculate efficiency matrices of the drivetrain system (both total matrices and single component matrices) for any desired set of speed, torque and temperature under steady state conditions.
Moreover, the tool can be used to efficiently analyse different cooling concepts (oil bath, wet-sump, and dry-sump system) in order to define the correct choice of pump and intercooler, thus ensuring a proper lubrication and cooling of the gearbox and electric motor.
Consequently, the tool allows to determine the optimal design of the whole ePowertrain in compliance with requested key performance indicators, such as efficiency, performance, packaging and cost.
Effectiveness of the tool has been verified by applying it to an industrial application, namely a single speed and cascade of gears transmission, which had already been bench-tested.
The efficiency matrices obtained analytically with the tool perfectly matched those obtained by bench testing the transmission, with an average deviation of less than 1%.