NVH analysis and optimization of an electric powertrain

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/305055
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Type: Examensarbete för masterexamen
Title: NVH analysis and optimization of an electric powertrain
Authors: Subramanian, Vignesh
Abstract: Because of its compact shape and high power density, the permanent magnet synchronous motor (PMSM) appears to be better as a drive motor for electric construction machines (ECMs). However, as compared to conventional construction machines, the motor-driven machine has exhibited a different noise profile as a result of the shift in noise excitation source. The dramatic increase in high-frequency noises is particularly noticeable in this scenario. Furthermore, some of these highfrequency noises are distributed in the frequency range that is sensitive to human hearing, causing operators to feel highly uneasy. Furthermore, gear whine noise emitted from the electric powertrain system could significantly affect driving comfort and has become an important NVH problem. The motor’s electromagnetic forces and the reducer’s gear-meshing forces could create structural vibration and whining noise with clear order characteristics. As a result, it’s critical to look into the NVH characteristics of PMSM and reducer for ECMs. Computer-aided engineering (CAE) based vibration and sound simulation is a key approach for studying and optimizing electric powertrain NVH behavior, especially in the early stages of design. To put it another way, being able to forecast the noise and vibration harshness (NVH) behavior of an electric drive used in ECMs without having to run experimental solutions can save a company a lot of time and money.
Keywords: NVH;PMSM;Electric Motor;ECMs;Powertrain
Issue Date: 2022
Publisher: Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper
Series/Report no.: 2022:48
URI: https://hdl.handle.net/20.500.12380/305055
Collection:Examensarbeten för masterexamen // Master Theses



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