This presentation was made at NAFEMS Americas Seminar "Engineering Analysis & Simulation in the Automotive Industry: Creating the Next Generation Vehicle Accurate Modelling for Tomorrow's Technologies".

The automotive engineering community is now confronting the largest technology transformation since its inception. This includes the electrification of powertrains for more efficient consumption and cleaner emissions, the reinvention of the battery with fast wireless charging capabilities and finally the advent of a fully autonomous vehicle. Compounding to these technology changes, the automotive companies design verification process is moving away from a major reliance on physical testing to almost a full virtual simulation product verification process. The challenges to the automotive engineers are enormous and require a significant increase in the upfront use of numerical simulation capabilities, methods and processes such they’re able to efficiently design, manufacture and deliver these very innovative technologies to the market in greater speeds than ever before.

Resource Abstract

Tonal noise such as motor whine and gear whine is more prominent for an electric drive (e-drive) powertrain in a HEV or BEV vehicle. The source of the motor whine is the motor electromagnetic forces, whereas the gear whine originates mainly from gear transmission errors. Structural response of the powertrain to the excitations can result in excessive noise which can negatively affect customer satisfaction.



This paper presents CAE analyses on such noise of a HEV transmission for an e-drive powertrain NVH assessment and improvement. Predicted motor forces from ANSYS Maxwell electromagnetic analysis are applied for assessing airborne and structure borne noises of the transmission. Gear NVH attribute is investigated through advanced Romax modeling and analysis. The CAE analyses are conducted throughout the HEV transmission development to guide in design selections for NVH, assessments against targets, and necessary counter measures for NVH improvements. The CAE predictions are validated through the correlations to test data.