Ultrafast finite-element analysis of brushless PM machines based on space-time transformations

Dan M. Ionel, Mircea Popescu

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

A computationally efficient method is proposed for the steady-state performance simulation of brushless permanent-magnet (PM) motors. Only a minimum number of magnetostatic finite-element (FE) analysis (FEA) solutions are used in conjunction with space–time transformations, which are based on the periodicity specific to synchronous machines. For an example electronically controlled interior PM (IPM) motor with six teeth per pole and a distributed winding, a single nonlinear magnetostatic FE solution was employed to estimate the flux density time waveforms in the stator teeth and yoke. Other results include core losses, back electromotive force, and torque. The extension of the method to fractional-slot topologies with reduced number of teeth and concentrated (non-overlapping) coils is discussed with reference to a nine-slot six-pole IPM motor example. The computational time is reduced by one to two orders of magnitude as compared with more laborious time-stepping FEA. Successful validation was performed against experimental data and detailed FEA results.

Original languageEnglish
Article number5676201
Pages (from-to)744-753
Number of pages10
JournalIEEE Transactions on Industry Applications
Volume47
Issue number2
DOIs
StatePublished - Mar 2011

Keywords

  • AC synchronous machine
  • back electromotive force (EMF)
  • brushless permanent-magnet (PM) (BLPM) motor
  • concentrated non-overlapping coils
  • core loss
  • distributed winding
  • finite-element (FE) analysis (FEA)
  • flux density waveform
  • interior PM (IPM) motor drive
  • surrogate optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Ultrafast finite-element analysis of brushless PM machines based on space-time transformations'. Together they form a unique fingerprint.

Cite this