Analytical Optimization of BLDC Motor Magnetic Parameters for Electric Vehicles Using the Whale Optimization Algorithm: PSO Comparison and Cost Analysis
Karadeniz Fen Bilimleri Dergisi, cilt.16, sa.3, ss.1222-1244, 2026 (TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 3
- Basım Tarihi: 2026
- Doi Numarası: 10.31466/kfbd.1840417
- Dergi Adı: Karadeniz Fen Bilimleri Dergisi
- Derginin Tarandığı İndeksler: TR DİZİN (ULAKBİM)
- Sayfa Sayıları: ss.1222-1244
- Recep Tayyip Erdoğan Üniversitesi Adresli: Evet
Özet
In this study, the magnetic design parameters of brushless DC (BLDC) motors for electric vehicle applications remanent flux density (Br), magnet coverage ratio (α), and air-gap length (g) are optimized using an analytical framework with the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO), within a search space defined by manufacturing and material feasibility limits reported in the literature. For each objective function (total harmonic distortion (THD), electromagnetic torque, and back electromotive force (back-EMF)), 100 independent runs are performed and evaluated using mean, variance, and standard deviation, supported by the Wilcoxon Signed-Rank test. The THD objective possesses a genuine interior optimum, where PSO converges more stably (variance 1.0420×10⁻⁵) while WOA shows stronger exploration (variance 3.2643×10⁻⁵); the Wilcoxon test (p = 1.86×10⁻⁹) confirms a statistically significant difference in favour of WOA. For torque and back-EMF, which are monotonic functions of the air-gap flux density, both algorithms converge to the same boundary-constrained optimum. From a practical perspective, the optimized parameters improve efficiency by about 2.5% and reduce NdFeB magnet usage by approximately 18% (~101 g), corresponding to an estimated saving of about $25 per motor.