A method to estimate the worst-case torque ripple under manufacturing uncertainties for permanent magnet synchronous machines

  • Yongxi Yang
  • , Nicola Bianchi
  • , Gerd Bramerdorfer
  • , Yong Kong
  • , Chengning Zhang
  • , Shuo Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

The influences of manufacturing uncertainties on the cogging torque of permanent-magnet (PM) motor have been widely examined, and they are usually estimated by calculating and comparing several design models featuring small deviations to their ideal counterpart. To achieve high quality of the analysis results within a reasonable calculation time, a suitable selection of design variants featuring uncertainties for evaluation are crucial. However, lack of knowledge of the relationship between torque variations and uncertainties, a proper selection is difficult to accomplish. In the previous work related to the worst-uncertaincombination-analysis (WUCA) method, efforts were made to reveal the relationship between the additional cogging torque harmonics and combinations of uncertainties. In this paper, the WUCA method is extended to estimate the on-load torque ripple performance under manufacturing uncertainties in this paper, and its effectiveness in terms of identifying the worst-case combinations is verified through finite element analysis.
Original languageEnglish
Title of host publicationIEEE ECCE 2020 Energy Conversion Congress and Exposition, Detroit, MI, USA
Pages4075-4082
Number of pages8
ISBN (Electronic)9781728158266
DOIs
Publication statusPublished - 11 Oct 2020

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
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Fields of science

  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202009 Electrical drive engineering
  • 202011 Electrical machines
  • 202025 Power electronics
  • 202027 Mechatronics

JKU Focus areas

  • Digital Transformation
  • Sustainable Development: Responsible Technologies and Management

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