Performance improvement of magnet temperature estimation using kernel method based non-linear parameter estimator for variable leakage flux ipmsms

Atsushi Okada, Ami S. Koshikawa, Kouki Yonaga, Kensuke Sasaki, Takashi Kato, Masayuki Ohzeki

Research output: Contribution to journalArticlepeer-review

Abstract

This study proposes a novel approach that employs the kernel method as a regression model to demonstrate the dependency of magnet flux linkage on the applied current, which is suitable for magnet temperature estimation. This model can estimate the flux linkage with a mean relative error of less than 2% in comparison with that obtained using finite element analysis. The magnet temperature is estimated by comparing the magnet flux linkage under loading conditions with the values obtained from the regression models built under fixed temperatures. The accuracy of the results obtained using the magnet temperature estimation method is approximately the same as that of the results obtained using the look-up table, suggesting that the proposed approach is suitable for non-linear motor property modeling.

Original languageEnglish
Pages (from-to)618-623
Number of pages6
JournalIEEJ Journal of Industry Applications
Volume10
Issue number6
DOIs
Publication statusPublished - 2021

Keywords

  • IPMSM
  • Machine learning
  • Temperature estimation
  • Variable Leakage Flux IPM (VLF-IPM)

ASJC Scopus subject areas

  • Automotive Engineering
  • Energy Engineering and Power Technology
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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