A simple behavior model for switched reluctance motors based on magnetic energy

Takuto Hara, Takayuki Kusumi, Kazuhiro Umetani, Eiji Hiraki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

A number of analytical models for switched reluctance motors have been proposed to promote development of control techniques that can alleviate the torque and source current ripples. However, these models can suffer from a large database of the nonlinearity as well as complicated derivation process of the torque and the voltage-current relation. The purpose of this paper is to propose a simple practical behavior model with small database and straightforward derivation of the torque and the voltage-current relation. The proposed model has a simple database of the magnetic energy as a matrix. The flux linkage and the electrical angle are chosen as the state variables. Along with theoretical formulation of the model, this paper presents a practical method of the model construction. In addition, experiments successfully predicted both torque and current waveforms, supporting appropriateness of the proposed model.

Original languageEnglish
Title of host publication2016 IEEE 8th International Power Electronics and Motion Control Conference, IPEMC-ECCE Asia 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages109-115
Number of pages7
ISBN (Electronic)9781509012107
DOIs
Publication statusPublished - Jul 13 2016
Event8th IEEE International Power Electronics and Motion Control Conference, IPEMC-ECCE Asia 2016 - Hefei, China
Duration: May 22 2016May 26 2016

Other

Other8th IEEE International Power Electronics and Motion Control Conference, IPEMC-ECCE Asia 2016
CountryChina
CityHefei
Period5/22/165/26/16

Keywords

  • analytical model
  • Lagrangian
  • magnetic energy
  • switched relutance motor

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Control and Optimization

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