Fuzzy Fitness Distance Balance Gradient-Based Optimization Algorithm (fFDBGBO): An Application to Design and Performance Optimization of PMSM

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dc.authoriduzel, hasan/0000-0002-8238-2588;
dc.contributor.authorUzel, Hasan
dc.contributor.authorHinislioglu, Yunus
dc.contributor.authorDalcali, Adem
dc.contributor.authorGuvenc, Ugur
dc.date.accessioned2025-10-11T20:48:15Z
dc.date.available2025-10-11T20:48:15Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThe exceptional properties of Permanent Magnet Synchronous Motors (PMSMs), including their small construction, high power-torque density, and high efficiency, make them one of the most popular electrical machines. However, the motor structure needs to be optimized for circumstances like boosting the PMSM's energy efficiency, optimizing output power, and lowering motor weight and cogging torque. This research aims to identify the optimal values for the parameters essential to achieving the most efficient design of a 15 kW PMSM. For this purpose, a new optimization algorithm is proposed. This proposed algorithm is an application of the fuzzy logic-based fitness distance balance design to the gradient-based optimization algorithm (GBO). The developed algorithm is called fuzzy Fitness Distance Balance Gradient-Based Optimization (fFDBGBO) algorithm. The proposed algorithm and various optimization algorithms with effective results in the literature were tested for the fitness function determined to find the optimal values of Embrace, Offset, Skew Width, Magnet Thickness, and slot bottom width (Bs1) of PMSM. The results indicate that the suggested algorithm achieves better performance than rival algorithms in terms of effectiveness. Compared to the GBO algorithm, the proposed fFDBGBO method achieves a 0.2979% increase in efficiency. Compared to other algorithms, the suggested algorithm's objective function has the highest slot fill factor (49.9995%), yet it still complies with the specified limit value. Relative to the initial design, the motor efficiency achieved with the fFDBGBO algorithm improved from 91.7788% to 93.0366%, while the magnet weight decreased from 2.14837 to 2.09475, and the total motor weight remained approximately the same as 53.2286 kg and 54.0436 kg.en_US
dc.identifier.doi10.1109/ACCESS.2025.3604649
dc.identifier.endpage155915en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-105015086911en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage155898en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2025.3604649
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21826
dc.identifier.volume13en_US
dc.identifier.wosWOS:001570481500029en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectOptimizationen_US
dc.subjectMotorsen_US
dc.subjectTorqueen_US
dc.subjectMagnetic fluxen_US
dc.subjectForgingen_US
dc.subjectGenetic algorithmsen_US
dc.subjectRotorsen_US
dc.subjectPermanent magnetsen_US
dc.subjectTorque measurementen_US
dc.subjectPermanent magnet motorsen_US
dc.subjectPermanent magnet synchronous motoren_US
dc.subjectmotor parameter optimizationen_US
dc.subjectgradient-based optimizationen_US
dc.subjectfuzzy fitness distance balanceen_US
dc.titleFuzzy Fitness Distance Balance Gradient-Based Optimization Algorithm (fFDBGBO): An Application to Design and Performance Optimization of PMSMen_US
dc.typeArticleen_US

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