Methods for Measuring, Analyzing and Predicting the Dynamic Torque of an Electric Drive Used in an Automotive Drivetrain

Event
SAE 2015 Noise and Vibration Conference and Exhibition
Authors Abstract
Content
The driving comfort is an important factor for buying decisions. For the interior noise of battery electric vehicles (BEV) high frequency tonal orders are characteristic. They can for example be caused by the gearbox or the electric drive and strongly influence the perception and rating of the interior noise by the customer.
In this contribution methods for measuring, analyzing and predicting the excitation by the dynamic torque of the electric drive are presented. The dynamic torque of the electric drive up to 3.5 kHz is measured on a component test bench with the help of high frequency, high precision torque transducer. The analysis of the results for the order of interest shows a good correlation with the acoustic measurements inside the corresponding vehicle. In addition an experimental and numerical modal analysis of the rotor of the electric drive are performed. The torsional resonance modes are put into context of the elevation of the dynamic torque with regard to the frequency. In a further analysis the phase shifts of the torsional excitation of the different rotor segments, caused by the skewing of the stator, are taken into account. The results show a good correlation with the elevation of the dynamic torque as well as the acoustic measurement for the order of interest.
The measurements, as well as the simulation, are integrated in a validation environment in context of the X-in-the-Loop framework. This enables a consistent and thorough validation of BEV interior noise in virtual as well as physical domain.
Meta TagsDetails
DOI
https://doi.org/10.4271/2015-01-2363
Pages
8
Citation
Albers, A., Fischer, J., Behrendt, M., and Lieske, D., "Methods for Measuring, Analyzing and Predicting the Dynamic Torque of an Electric Drive Used in an Automotive Drivetrain," SAE Int. J. Alt. Power. 4(2):363-369, 2015, https://doi.org/10.4271/2015-01-2363.
Additional Details
Publisher
Published
Jun 15, 2015
Product Code
2015-01-2363
Content Type
Journal Article
Language
English