Iron loss and temperature field analysis of four-phase 8/6 structure switched reluctance motor for electric vehicles

Hao Chen, Wenju Yan, C. C. Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

This paper studies about the influence of different polarity distribution on the iron loss and temperature field on a four-phase 8/6 structure switched reluctance machine for electric vehicles. Firstly, a switched reluctance iron loss mathematical calculation model is proposed by fitting the core silicon sheet iron loss data of the prototype. Then, a 2D finite element model is constructed to analyze the magnetic flux density waveforms of iron core. Magnetic flux densities of the iron core under two different magnetic poles distributions are also compared. The iron loss of the different magnetic pole distribution is calculated respectively. Finally, the 2D finite element thermal model of the prototype is established and the simulation results are analyzed. Then, the experimental test platform is built. The experimental results show that the temperature rise is in good agreement with the simulated temperature rise.

Original languageEnglish
Title of host publication2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728112497
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Hanoi, Viet Nam
Duration: 14 Oct 201917 Oct 2019

Publication series

Name2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings

Conference

Conference2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019
Country/TerritoryViet Nam
CityHanoi
Period14/10/1917/10/19

Keywords

  • Iron loss
  • Magnetic analysis
  • Motor control
  • Switched reluctance
  • Thermal analysis

ASJC Scopus subject areas

  • Control and Optimization
  • Modelling and Simulation
  • Artificial Intelligence
  • Transportation
  • Energy Engineering and Power Technology
  • Fuel Technology
  • Automotive Engineering
  • Electrical and Electronic Engineering

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