Computationally Efficient Estimation of PWM-induced Iron Loss of PMSM using Deep Transfer Learning

Soo Hwan Park, Ki O. Kim, Myung Seop Lim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As the demand for increasing the efficiency of traction motor for increasing the mileage of electric vehicles, it is necessary to accurately estimate the efficiency of traction motor at the early design stage. Since the iron loss of the traction motor is highly affected by the pulse width modulation (PWM) frequency, the PWM current should be considered when designing the motor. However, it is difficult in considering the PWM current at early design stage because of its high computation cost. Therefore, we propose a method to reduce the computation cost for the calculation of PWM-induced iron loss using deep transfer learning. The proposed method can be achieved by training a neural network that can predict PWM-induced iron loss accurately using a large amount of sinusoidal current-based iron loss and a small amount of PWM-induced iron loss. As a result, the PWM current can be practically considered in design stage of traction motor because the computation cost can be decreased by using the proposed method.

Original languageEnglish
Title of host publication2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350338362
DOIs
StatePublished - 2023
Event2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023 - Sendai, Japan
Duration: 15 May 202319 May 2023

Publication series

Name2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023 - Proceedings

Conference

Conference2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023
Country/TerritoryJapan
CitySendai
Period15/05/2319/05/23

Keywords

  • Deep neural network (DNN)
  • iron loss
  • PWM current
  • transfer learning

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