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\begin{document}

\title{Automatic Design System with Generative Adversarial Network and Vision
Transformer for Efficiency Optimization of Interior Permanent Magnet
Synchronous Motor}



\author[1]{Yuki Shimizu }%
\affil[1]{Ritsumeikan University}%


\vspace{-1em}



  
  \date{June 11, 2024}


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\begin{abstract}
Interior permanent magnet synchronous motors are becoming increasingly
popular as traction motors in environmentally friendly vehicles. These
motors, which offer a wide range of design options, require
time-consuming finite element analysis to verify their performance,
thereby extending design times. To address this problem, we propose a
deep learning model that can accurately predict the iron loss
characteristics of different rotor topologies under various speed and
current conditions, resulting in an automatic design system for the
IPMSM rotor core. Using this system, the computation time for efficiency
maps is reduced to less than 1/3000 of the time required for finite
element analysis. The system also shows efficiency optimization results
similar to the best results of previous research, while reducing the
computational time for optimization by one or two orders of magnitude.%
\end{abstract}%



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