Multimode Optimization of Switched Reluctance Machines in Hybrid Electric Vehicles

Diao, K; Sun, X; Lei, G; Guo, Y; Zhu, J

Multimode Optimization of Switched Reluctance Machines in Hybrid Electric Vehicles

Diao, K Sun, X Lei, G

Guo, Y

Zhu, J

Permalink

Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Energy Conversion, 2021, 36, (3), pp. 2217-2226
Issue Date:: 2021

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 30 Sep 2023

Adobe PDF

Download Accepted versionAdobe PDF (834.81 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Diao, K
dc.contributor.author	Sun, X
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.date.accessioned	2022-01-30T09:41:56Z
dc.date.available	2022-01-30T09:41:56Z
dc.date.issued	2021
dc.identifier.citation	IEEE Transactions on Energy Conversion, 2021, 36, (3), pp. 2217-2226
dc.identifier.issn	0885-8969
dc.identifier.issn	1558-0059
dc.identifier.uri	http://hdl.handle.net/10453/153872
dc.description.abstract	IEEE The belt-driven starter/generator (BSG), as a cost-effective solution, has been widely employed in hybrid electric vehicles (HEVs) to improve the stability and reduce the fuel consumption of the vehicles. It can provide more than 10% reduction in CO2. Electrical machine is the heart of the BSG system, which is functioned both as motor and generator. In order to optimize both aspects of motor and generator simultaneously, this paper presents a new multimode optimization method for the switched reluctance machines. First, the general multimode concept and optimization method are presented. The switched reluctance motor and the switched reluctance generator are the two operation modes. The optimization models are established based on motor and generator functions. Sensitivity analysis, surrogate models and genetic algorithms are employed to improve the efficiency of the multimode optimization. Then, a design example of a segmented-rotor switched reluctance machine (SSRM) is investigated. Seven design variables and four driving modes are considered in the multiobjective optimization model. The Kriging model is employed to approximate the finite element model (FEM) in the optimization. Finally, the optimization results are depicted, and an optimal solution is selected. The comparison between the initial and optimal designs shows that the proposed method can improve the foremost performance of the SSRM under all driving modes.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Energy Conversion
dc.relation.isbasedon	10.1109/TEC.2020.3046721
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Multimode Optimization of Switched Reluctance Machines in Hybrid Electric Vehicles
dc.type	Journal Article
utslib.citation.volume	36
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2023-09-30T00:00:00+1000Z
dc.date.updated	2022-01-30T09:41:49Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	36
utslib.citation.issue	3

Abstract:

IEEE The belt-driven starter/generator (BSG), as a cost-effective solution, has been widely employed in hybrid electric vehicles (HEVs) to improve the stability and reduce the fuel consumption of the vehicles. It can provide more than 10% reduction in CO2. Electrical machine is the heart of the BSG system, which is functioned both as motor and generator. In order to optimize both aspects of motor and generator simultaneously, this paper presents a new multimode optimization method for the switched reluctance machines. First, the general multimode concept and optimization method are presented. The switched reluctance motor and the switched reluctance generator are the two operation modes. The optimization models are established based on motor and generator functions. Sensitivity analysis, surrogate models and genetic algorithms are employed to improve the efficiency of the multimode optimization. Then, a design example of a segmented-rotor switched reluctance machine (SSRM) is investigated. Seven design variables and four driving modes are considered in the multiobjective optimization model. The Kriging model is employed to approximate the finite element model (FEM) in the optimization. Finally, the optimization results are depicted, and an optimal solution is selected. The comparison between the initial and optimal designs shows that the proposed method can improve the foremost performance of the SSRM under all driving modes.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/153872