Weiduo Zhao
Multi-Physics and Multi-Objective Optimization of a High Speed PMSM for High Performance Applications
Zhao, Weiduo; Wang, Xuejiao; Gerada, Chris; Zhang, He; Liu, Chuan; Wang, Yinli
Authors
Xuejiao Wang
CHRISTOPHER GERADA CHRIS.GERADA@NOTTINGHAM.AC.UK
Professor of Electrical Machines
He Zhang
Chuan Liu
Yinli Wang
Abstract
© 1965-2012 IEEE. High-speed permanent magnet synchronous machine (PMSM) can provide high power density and high efficiency, which is often highly desirable in high performance applications. A multi-physics optimization program based on the multi-objective genetic algorithm was developed in this paper, to achieve a tradeoff solution between the electromagnetic, mechanical, and thermal aspects. First, the parametric electromagnetic model was modeled based on the finite-element method, and then a thermal network model and an analytical mechanical model to determine the thickness of the magnet and the sleeve were developed and merged within a design cycle of the machine, in an effort to attain the target performances of 20 kW/kg at 20 000 r/min for a 2 MW PMSM. Optimization results indicated that a final design with eight poles and 48 slots could obtain a comprehensive performance between power density and efficiency, and the performance satisfied all the requirements.
Citation
Zhao, W., Wang, X., Gerada, C., Zhang, H., Liu, C., & Wang, Y. (2018). Multi-Physics and Multi-Objective Optimization of a High Speed PMSM for High Performance Applications. IEEE Transactions on Magnetics, 54(11), Article 8106405. https://doi.org/10.1109/TMAG.2018.2835504
Journal Article Type | Article |
---|---|
Acceptance Date | May 8, 2018 |
Online Publication Date | May 30, 2018 |
Publication Date | 2018-11 |
Deposit Date | Apr 27, 2020 |
Publicly Available Date | Apr 27, 2020 |
Journal | IEEE Transactions on Magnetics |
Print ISSN | 0018-9464 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 54 |
Issue | 11 |
Article Number | 8106405 |
DOI | https://doi.org/10.1109/TMAG.2018.2835504 |
Public URL | https://nottingham-repository.worktribe.com/output/1221416 |
Publisher URL | https://ieeexplore.ieee.org/document/8369390 |
Additional Information | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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