An Innovative Approach to Electrical Motor Geometry Generation Using Machine Learning and Image Processing Techniques

Ugur Demir, Gazi Akgun, Mustafa Caner Akuner, Majid Pourkarimi, Omer Akgun, Tahir Cetin Akinci*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This paper presents a methodology for generating geometries for interior permanent magnet (IPM) motors in electric vehicles (EVs) through the application of artificial intelligence (AI) and image processing (IP) techniques. Due to the implementation of green agreements and policies aimed at reducing greenhouse gas emissions, EVs have become popularity. As a consequence, the improvement studies on the powertrain and battery system of EVs are focused. Especially for the powertrain, design optimization studies of e-motor have increased in the literature. One of the most widely used e-motor topologies is interior permanent magnet (IPM) motor. However, designing the IPM motor presents a challenge due to the dynamic considerations with geometric constraints. Therefore, e-motor designers encounter challenges related to determining initial geometry and the long time of the optimization process. To address these challenges, a novel approach is proposed that leverages machine learning (ML) techniques in combination with IP to generate initial geometries and design parameters for IPM motors. The proposed approach generates images of the motor geometry and extract dimensional features from the resulting images by using artificial neural networks (ANNs). The proposed method performs an analysis of the input vectors to reduce their size using techniques such as Histogram, 2D Maximum, 2D Mean, 2D Minimum, 2D Standard Deviation, and 2D Variance to enhance feature extraction. Additionally, FFT (Fast Fourier Transform) and IFFT (Inverse Fast Fourier Transform) are used to improve the neural network process in generating the image geometry. Further, the generated image geometry is improved by applying digital filtering techniques such as Log, FFT, Log+FFT, Laplacian, Sobel, and Histogram Equalization. Finally, the trained ANNs are tested to validate the results by using Ansys RMXprt and Maxwell. Eventually, the proposed method represents an innovative solution to generating initial geometries for IPM motors in EVs that satisfies desired design requirements. This approach leverages the power of AI and image processing techniques, which could lead to significant improvements in the optimization process for IPM motors, accelerate the designer's analysis process, and enhance the performance of EVs.

Original languageEnglish
Pages (from-to)48651-48666
Number of pages16
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • 2D filter
  • Artificial neural network
  • feature extraction
  • image generation
  • interior permanent magnet motor
  • machine learning

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