Optimal Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicle Applications Using a Machine Learning-Based Surrogate Model

Autor:	Song Guo, Xiangdong Su, Hang Zhao
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	interior permanent magnet synchronous motor optimization machine learning Technology
Zdroj:	Energies, Vol 17, Iss 16, p 3864 (2024)
Druh dokumentu:	article
ISSN:	1996-1073
DOI:	10.3390/en17163864
Popis:	This paper presents an innovative design for an interior permanent magnet synchronous motor (IPMSM), targeting enhanced performance for electric vehicle (EV) applications. The proposed motor features a double V-shaped rotor structure with irregular ferrite magnets embedded in the slots between the permanent magnets. This design significantly enhances torque performance. Furthermore, a machine learning-based surrogate model is developed by integrating fine and coarse mesh data. Optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this surrogate model effectively reduces computational time compared to traditional finite element analysis (FEA).
Databáze:	Directory of Open Access Journals
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