PMSM Noise - Simulation Measurement Comparison

2018-01-1552

06/13/2018

Event
10th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference
Authors Abstract
Content
Growing development of hybrid and fully electrical drives increases demand for accurate prediction of noise and vibration characteristic of electric and electronic components. This paper describes the numerical and experimental investigation of noise emission from PMSM electric machine as a one of the most important noise sources in electric vehicles.
Structural and air borne noise is measured on e-machine test rig and used for calibration and validation of the numerical model. The electro-magnetic field in PMSM is simulated using finite volume method. Electro-magnetic forces are applied as excitation to the 3D FE model of e-machine, mounded on test frame. Material properties are tuned using results from experimental modal analysis including identification of orthotropic characteristic of stator laminated core, assembled together with coil and end winding. Structural vibrations are calculated by modal frequency response analysis and applied as excitation in air borne noise simulation. Sound radiation is calculated using the wave based technique approach (WBT). Simulation and experimental results are compared in frequency range up to 6 kHz.
Developed simulation methodology can successfully predict the main noise sources from e-machine and results in good agreement in absolute values of sound pressure level. Stator 0th order diametrical mode, also called stator breathing mode, is identified as critical noise source, both in experiment and simulation.
Meta TagsDetails
DOI
https://doi.org/10.4271/2018-01-1552
Pages
8
Citation
Klarin, B., Knaus, K., Schneider, J., Diwoky, F. et al., "PMSM Noise - Simulation Measurement Comparison," SAE Technical Paper 2018-01-1552, 2018, https://doi.org/10.4271/2018-01-1552.
Additional Details
Publisher
Published
Jun 13, 2018
Product Code
2018-01-1552
Content Type
Technical Paper
Language
English