JOURNAL ARTICLE

Equivalent Analytical Model of Magnetic Field in Parallel Magnetic Circuit Axial Flux Permanent Magnet Machines.

  • Published In: IEEJ Transactions on Electrical & Electronic Engineering, 2024, v. 19, n. 6. P. 1079 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Xiaoxiao, Zhao; Xiaoyuan, Wang; Peng, Gao; Tianyuan, Li 3 of 3

Abstract

The axial flux permanent magnet (AFPM) machines with the parallel magnetic circuit (PMC) rotor can effectively improve its torque density. The PMC rotor consists of two sub‐rotors, radial Halbach array permanent magnet (PM) and tangential PM. The magnetic fields generated by two sub‐rotors are not in the same 2‐D analytical planes. In this paper, an equivalent analytical model of PMC‐AFPM machines is proposed to solve this problem. The radial Halbach array PM rotor is equated to the axial Halbach array PM rotor based on the equivalent transformation principle. The exact subdomain models of equivalent axial Halbach array PM rotor and tangential PM rotor are built respectively and then superimposed to obtain the magnetic field distribution. The electromagnetic characteristics of the PMC‐AFPM machine under no‐load and load conditions are calculated by the proposed analytical model and compared with calculations of the finite element model (FEM). The results verify the accuracy of the equivalent analytical model. The comparative analysis of the AFPM machines with PMC rotor and tangential PM rotor verifies the advantage of PMC rotor configuration to improve the torque density. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:IEEJ Transactions on Electrical & Electronic Engineering. 2024/06, Vol. 19, Issue 6, p1079
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2024
  • ISSN:1931-4973
  • DOI:10.1002/tee.24052
  • Accession Number:177061132
  • Copyright Statement:Copyright of IEEJ Transactions on Electrical & Electronic Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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