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|Title:||Comparative study and design optimization of a dual-mechanical-port electric machine for hybrid electric vehicle applications||Authors:||Chen, Hao
El-Refaie, Ayman M.
Lee, Christopher Ho Tin
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2022||Source:||Chen, H., El-Refaie, A. M., Zuo, Y., Cai, S., Cao, L. & Lee, C. H. T. (2022). Comparative study and design optimization of a dual-mechanical-port electric machine for hybrid electric vehicle applications. IEEE Transactions On Vehicular Technology, 71(8), 8341-8353. https://dx.doi.org/10.1109/TVT.2022.3175476||Project:||NRF-NRFF12-2020-0003||Journal:||IEEE Transactions on Vehicular Technology||Abstract:||A new dual-mechanical-port (DMP) electric machine for hybrid electric vehicle applications, particularly in the power-split continuously variable transmission systems, is proposed in this paper. In order to comprehensively and quantitatively evaluate the pros and cons of the proposed machine, a comparative study of four DMP electric machines with different topologies is conducted. These four investigated DMP electric machines include a conventional DMP machine, a DMP machine with spoke-type permanent magnets, a DMP machine with reluctance rotor, and a DMP machine with open slots which is the proposed machine in this paper. Even though these four machines have similar topologies, they have different operating principles, which are demonstrated in detail. The comparison results indicate that the DMP machine with open slots outperforms the others in terms of torque/power density, efficiency, magnet utilization, etc. Accordingly, the DMP machine with open slots is selected for further investigation and optimization. A large-scale multi-objective optimization is carried out for this machine, where the differential evolution algorithm serves as a global search engine to target optimal performance. Finally, an optimal design is prototyped, and the experimental results are performed to verify the effectiveness of the analysis and simulation results in this paper.||URI:||https://hdl.handle.net/10356/161188||ISSN:||0018-9545||DOI:||10.1109/TVT.2022.3175476||Schools:||School of Electrical and Electronic Engineering||Rights:||© 2022 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. The published version is available at: https://doi.org/10.1109/TVT.2022.3175476.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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