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: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes the novel approach employing the kernel method as regression model to describe the dependency of magnet flux linkage on applied current, which is suitable for magnet temperature estimation. The model estimates flux linkage values with mean relative error of less than 2% compared to values calculated from finite element analysis (FEA). Magnet temperature is estimated by comparing a magnet flux linkage under loaded condition to the values from the regression models built under fixed temperatures. Results of the magnet temperature estimation method is about the same accuracy of the results using look-up table (LUT), hence it suggests the approach is suitable for non-linear motor property modeling.

Original languageEnglish
Title of host publication23rd International Conference on Electrical Machines and Systems, ICEMS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1957-1960
Number of pages4
ISBN (Electronic)9784886864192
DOIs
Publication statusPublished - 2020 Nov 24
Event23rd International Conference on Electrical Machines and Systems, ICEMS 2020 - Hamamatsu, Japan
Duration: 2020 Nov 242020 Nov 27

Publication series

Name23rd International Conference on Electrical Machines and Systems, ICEMS 2020

Conference

Conference23rd International Conference on Electrical Machines and Systems, ICEMS 2020
Country/TerritoryJapan
CityHamamatsu
Period20/11/2420/11/27

Keywords

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

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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