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

Abstract

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