Optimizing the Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicles with a Hybrid ABC-SVM Algorithm

This paper presents a comprehensive investigation of the optimal design an interior permanent magnet synchronous motor (IPMSM) for electric vehicles (EVs), utilizing hybrid artificial bee colony algorithm–support vector machine (HAS) algorithm. The performance drive is crucial determinant overall vehicle performance, particularly in EVs that rely solely on propulsion. In this context, motors (IPMSMs) offer compelling choice due to their high torque density, wide speed range, superior efficiency, and robustness. However, accurate analysis nonlinear characteristics IPMSMs necessitates finite element analysis, which can be time-consuming. Therefore, research into methods deriving model with minimal computation significant importance. HAS powerful multimodal optimization technique capable exploring several solutions. It enhances navigation capability by combining algorithm (ABC) kernel support (KSVM). Specifically, improves search ability optimizing movement bees each region generated KSVM. Furthermore, hybridization Nelder–Mead method ensures quick convergence at pointers discovered ABC. To demonstrate effectiveness proposed algorithm, study compared its conventional two mathematical test functions, verifying remarkable performance. Finally, was applied IPMSM EVs. Overall, provides thorough application vehicles, expected benefit from combination machine-learning techniques various other algorithms.