Design of an Unequal-Teeth Stator Structure for a Low-Vibration Noise Permanent Magnet Synchronous Machine Considering Teeth Modulation

To address the high vibration and noise in fractional-slot concentrated-winding permanent magnet synchronous machines for electric vehicles, this study focuses on a 30-pole, 36-slot fractional-slot concentrated-winding permanent magnet synchronous machine. These issues are mainly caused by the modul...

Full description

Saved in:
Bibliographic Details
Main Authors: Liyan Guo, Xiangyi Li, Huatuo Zhang, Huimin Wang, Zhichen Lin, Tao Zhang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:World Electric Vehicle Journal
Subjects:
Online Access:https://www.mdpi.com/2032-6653/16/7/339
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To address the high vibration and noise in fractional-slot concentrated-winding permanent magnet synchronous machines for electric vehicles, this study focuses on a 30-pole, 36-slot fractional-slot concentrated-winding permanent magnet synchronous machine. These issues are mainly caused by the modulation of high-order radial electromagnetic forces into low-order radial electromagnetic forces, known as the teeth modulation effect. The characteristics of radial electromagnetic forces are analyzed using the Maxwell stress tensor method, and the modulation process is examined. A novel unequal-teeth stator structure is proposed to reduce vibration and noise. Finite element simulations are performed to investigate how this structure affects the amplitude of modulated low-order radial electromagnetic forces. The optimal ratio of the unequal-teeth design is identified to effectively suppress the modulation effect. Simulation results indicate that an appropriately chosen unequal-teeth proportion leads to significant improvements in the machine’s vibration and noise performance across various operating conditions, providing a preliminary validation of the feasibility and effectiveness of the proposed unequal-teeth design methodology.
ISSN:2032-6653