Torque Modeling of a Segmented-Rotor SRM Using Maximum-Correntropy-Criterion-Based LSSVR for Torque Calculation of EVs

Sun, X; Wu, J; Lei, G; Cai, Y; Chen, X; Guo, Y

Torque Modeling of a Segmented-Rotor SRM Using Maximum-Correntropy-Criterion-Based LSSVR for Torque Calculation of EVs

Sun, X Wu, J Lei, G Cai, Y Chen, X Guo, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Journal of Emerging and Selected Topics in Power Electronics, 2021, 9, (3), pp. 2674-2684
Issue Date:: 2021-06-01

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Field	Value	Language
dc.contributor.author	Sun, X
dc.contributor.author	Wu, J
dc.contributor.author	Lei, G
dc.contributor.author	Cai, Y
dc.contributor.author	Chen, X
dc.contributor.author	Guo, Y
dc.date.accessioned	2022-05-07T09:46:32Z
dc.date.available	2022-05-07T09:46:32Z
dc.date.issued	2021-06-01
dc.identifier.citation	IEEE Journal of Emerging and Selected Topics in Power Electronics, 2021, 9, (3), pp. 2674-2684
dc.identifier.issn	2168-6777
dc.identifier.issn	2168-6785
dc.identifier.uri	http://hdl.handle.net/10453/157106
dc.description.abstract	High nonlinearities of switched reluctance motor (SRM) caused by its double salient structure limit its industrial application in electric vehicles (EVs). In this article, an algorithm called maximum-correntropy-criterion-based least-squares support vector regression (MCC-LSSVR) is applied to the nonlinear modeling of a segmented-rotor SRM (SSRM). First, the mathematical model of SSRM is established. Finite element analysis (FEA) is carried out to obtain the static flux linkage and torque. Then, the intelligent algorithm MCC-LSSVR using an adaptive weight to avoid the interference of outliers is introduced. It is verified and applied to SSRM modeling. The results show that MCC-LSSVR exhibits a better performance than other intelligent algorithms. Finally, simulation and experimental validation under various modes are given to verify the accuracy and effectiveness of the MCC-LSSVR model. It is shown that the simulation and experimental results are in good agreement.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Journal of Emerging and Selected Topics in Power Electronics
dc.relation.isbasedon	10.1109/JESTPE.2020.2977957
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.title	Torque Modeling of a Segmented-Rotor SRM Using Maximum-Correntropy-Criterion-Based LSSVR for Torque Calculation of EVs
dc.type	Journal Article
utslib.citation.volume	9
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2023-06-01T00:00:00+1000Z
dc.date.updated	2022-05-07T09:45:45Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	3

Abstract:

High nonlinearities of switched reluctance motor (SRM) caused by its double salient structure limit its industrial application in electric vehicles (EVs). In this article, an algorithm called maximum-correntropy-criterion-based least-squares support vector regression (MCC-LSSVR) is applied to the nonlinear modeling of a segmented-rotor SRM (SSRM). First, the mathematical model of SSRM is established. Finite element analysis (FEA) is carried out to obtain the static flux linkage and torque. Then, the intelligent algorithm MCC-LSSVR using an adaptive weight to avoid the interference of outliers is introduced. It is verified and applied to SSRM modeling. The results show that MCC-LSSVR exhibits a better performance than other intelligent algorithms. Finally, simulation and experimental validation under various modes are given to verify the accuracy and effectiveness of the MCC-LSSVR model. It is shown that the simulation and experimental results are in good agreement.

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http://hdl.handle.net/10453/157106