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Design and analysis of linear parameter varying control for IPMSM using new European driving cycle

  • Universiti Sains Malaysia
  • Ohio State University
  • Capital University of Science & Technology
  • University of Engineering and Technology, Taxila

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Electric traction motors are the most integral part of fully electric vehicles. Among all traction motors, based on high efficiency, power density, reliability, high torque-to-inertia ratio and control maturity, interior permanent magnet synchronous machine (IPMSM) has proved to be one of the most suitable choices. This paper compares and discusses the various state-of-the-art IPMSM control strategies based on key criteria such as robustness, performance, degree of complexity, and hardware implementation. In this paper, a robust gain scheduling LPV controller is designed for a nonlinear IPMSM in d–q reference frame taking into account the thermal effects. Linear Matrix Inequalities are used for synthesis conditions. The robust gain scheduling LPV adopts induced L2 / L -norm performance specifications in LPV framework using stator resistance as scheduling time varying parameter. The LPV controller results are validated by comparing them to proportional integral derivative and linear quadratic integrator control techniques using the new European driving cycle.

Original languageEnglish
Article number368
JournalJournal of the Brazilian Society of Mechanical Sciences and Engineering
Volume45
Issue number7
DOIs
StatePublished - Jul 2023
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Fully electric vehicles
  • Interior permanent magnet synchronous machine
  • Linear matrix inequalities (LMI)
  • Linear parameter varying
  • Linear quadratic integrator (LQI)
  • New European driving cycle (NEDC)
  • Proportional integral derivative (PID)

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