Optimization of PMSM Design Parameters Using Genetic Algorithms and FEM: A Study on Cogging Torque Reduction and Magnetic Flux Distribution Enhancement

Abstract

This study investigates the optimization of Permanent Magnet Synchronous Motors (PMSM) design parameters using Genetic Algorithms (GA) combined with Finite Element Method (FEM) simulations to enhance motor performance and reliability. By focusing on critical parameters such as H0 (slot opening) and pole embrace, the research aims to minimize cogging torque—a key factor contributing to vibration, noise, and efficiency loss in PMSMs—while improving the uniformity of magnetic flux distribution. The GA-driven approach explores diverse design configurations systematically, enabling the identification of optimal parameter sets. Results demonstrate a significant reduction in cogging torque from 1.11 Nm to 0.23 Nm and a more uniform magnetic flux distribution, indicating improved motor efficiency and reduced mechanical stress. These findings highlight the effectiveness of GA in addressing PMSM design challenges and underscore its potential as a valuable tool for advancing the development of high-performance, energy-efficient electric motors.