Surrogate-Assisted Multi-Objective Optimization of Interior Permanent Magnet Synchronous Motors with a Limited Sample Size

Interior permanent magnet synchronous motors (IPMSMs) are critical for electric vehicle traction and industrial systems, yet optimizing their performance under high-dimensional design spaces remains computationally challenging. This study proposes a surrogate-assisted multi-objective optimization fr...

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Autors principals:	Zhiyong Li, Mingfeng Huang, Ziyi Wang
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2025-04-01
Col·lecció:	Applied Sciences
Matèries:	multi-objective optimization of motors surrogate-assisted optimization generalized regression neural network (GRNN) Latin hypercube sampling (LHS)
Accés en línia:	https://www.mdpi.com/2076-3417/15/8/4259
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