Induction Motor Geometric Parameter Optimization Using a Metaheuristic Optimization Method for High-Efficiency Motor Design

Hasbi Apaydin (), Necibe Füsun Oyman Serteller and Yüksel Oğuz
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Hasbi Apaydin: Faculty of Technology, Department of Electrical and Electronics Engineering, Marmara University, Istanbul 34843, Turkey
Necibe Füsun Oyman Serteller: Faculty of Technology, Department of Electrical and Electronics Engineering, Marmara University, Istanbul 34843, Turkey
Yüksel Oğuz: Faculty of Technology, Department of Electrical and Electronics Engineering, Afyon Kocatepe University, Afyonkarahisar 03200, Turkey

Energies, 2025, vol. 18, issue 3, 1-17

Abstract: In this study, the optimum design for an induction motor (IM) was realized by providing details of its geometric design. The IM optimization was carried out using the Artificial Ecosystem-based Optimization (AEO) algorithm, a metaheuristic method. The AEO algorithm was used for the first time in IM optimization, and the design parameters were optimized. Ten motor design parameters were used as design variables. IM efficiency was improved, as the objective function. The genetic algorithm (GA) optimization method was used for comparison with the results obtained with the AEO method. The optimized and unoptimized results of the IM design generated with codes created in the Matlab program were verified with the Ansys RMxprt EM Suite 19.2 program, and it could be seen that the results are in good agreement. As a result of these studies, it was observed that the use of AEO in determining the geometric parameters of the IM had better convergence accuracy and reached the optimum result in a shorter time compared to the GA optimization method. It was observed that IM efficiency increased from 90.34% to 91.575% on average with the AEO method.

Keywords: induction motor design; motor optimization; genetic algorithm; artificial ecosystem optimization; geometric parameter optimization; high-efficiency motor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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