High-Fidelity Model Identification for Synchronous Reluctance Motor Drives

Vasyl Varvolik, Giampaolo Buticchi, Shuo Wang, Dmytro Prystupa, Sergei Peresada, Serhiy Bozhko, Michael Galea

Research output: Journal PublicationArticlepeer-review


This article presents an accurate model identification method for the synchronous reluctance machine (SynRel), considering the nonlinear magnetic behavior and spatial harmonics. The availability of the high-fidelity magnetic model is essential for high-performance control and modeling the vibro-acoustic behavior of the electric drive, allowing to predict not only the average torque but also the torque ripple behavior versus rotor position. The magnetic coenergy variation is calculated based on the flux linkages in order to yield the torque ripple. During the identification, the current disturbances introduced by the spatial effects and the inverter nonlinearities are properly suppressed by the merged proportional-integral (PI) controller with a repetitive technique. Besides, the inverter nonlinearity and digital control effects are carefully addressed to improve identification accuracy. The effectiveness of the proposed identification method is demonstrated by the comparison of the finite element (FE) simulation results and experiments for two SynRel prototypes.

Original languageEnglish
Pages (from-to)2623-2633
Number of pages11
JournalIEEE Transactions on Energy Conversion
Issue number4
Publication statusPublished - 1 Dec 2023


  • Flux linkage identification
  • repetitive control
  • spatial harmonics
  • synchronous reluctance machine (SynRel)
  • torque ripple

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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