Research Article
Optimization Design of Surface-mounted Permanent Magnet Synchronous Motors Using Genetic Algorithms
@ARTICLE{10.4108/ew.4864, author={Trinh Truong Cong and Thanh Nguyen Vu and Gabriel Pinto and Vuong Dang Quoc}, title={Optimization Design of Surface-mounted Permanent Magnet Synchronous Motors Using Genetic Algorithms}, journal={EAI Endorsed Transactions on Energy Web}, volume={11}, number={1}, publisher={EAI}, journal_a={EW}, year={2024}, month={1}, keywords={Permanent magnet sychronous motor, PMSM, Surface-mounted permanent magnet synchronous motor, SPMSM, genetic algorithm, GA, finite element method}, doi={10.4108/ew.4864} }
- Trinh Truong Cong
Thanh Nguyen Vu
Gabriel Pinto
Vuong Dang Quoc
Year: 2024
Optimization Design of Surface-mounted Permanent Magnet Synchronous Motors Using Genetic Algorithms
EW
EAI
DOI: 10.4108/ew.4864
Abstract
The permanent magnet synchronous motor (PMSM) has gained widespread popularity in various industrial applications due to its simple structure, reliable performance, compact size, high efficiency, and adaptability to different shapes and sizes. Its exceptional characteristics have made it a focal point in industrial settings. The PMSM can be categorized into two primary types based on the arrangement of the permanent magnets (PM): interior permanent magnet (IPM) and surface-mounted permanent magnet (SPM). In the IPM, the magnets are embedded into the rotor, while in SPM, they are mounted on the rotor's surface. The utilization of PMs eliminates the need for excitation currents due to their high flux density and significant coercive force. This absence of excitation losses contributes to a notable increase in efficiency. In this study, a multi-objective optimal design approach is introduced for a surface mounted PMSM, aiming to achieve maximum efficiency while minimizing material costs. The optimization task is accomplished using a genetic algorithm. Furthermore, the motor designs are simulated using the finite element method (FEM) to assess and compare designs before and after the optimization process.
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