A Machine Learning Modeling Method for Switched Reluctance Motors Based on Few Preprocessed Flux Linkage Data

This article proposes a machine learning method to build the model of a switched reluctance motor (SRM) using few preprocessed flux linkage data. Firstly, the improved torque balance method is used to obtain the accurate flux linkage data without redundant experiments. Secondly, two special data pre...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of electrical engineering & technology Vol. 20; no. 5; pp. 3445 - 3456
Main Authors:	zhao, Yan, Zhu, Jingwei, Ren, Ping, Jing, Zhe
Format:	Journal Article
Language:	English
Published:	Singapore Springer Nature Singapore 01.07.2025 Springer Nature B.V 대한전기학회
Subjects:	Electrical Engineering Electrical Machines and Networks Electronics and Microelectronics Engineering Instrumentation Original Article Power Electronics 전기공학 Nonlinear modeling Switched reluctance motor Data preprocessing Improved tuna swarm algorithm Support vector regression algorithm
ISSN:	1975-0102, 2093-7423
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	This article proposes a machine learning method to build the model of a switched reluctance motor (SRM) using few preprocessed flux linkage data. Firstly, the improved torque balance method is used to obtain the accurate flux linkage data without redundant experiments. Secondly, two special data preprocessing steps are proposed, which are nonlinear preprocessing and angle mapping, respectively. The first step provides beneficial nonlinearity for algorithms, and the second step improves the linearity of flux linkage at small angles by the proposed mapping function. Thirdly, support vector regression algorithm optimized by the improved tuna swarm algorithm (ITSO-SVR) is employed to establish the flux linkage model. Based on the flux linkage model, the current and torque models are easily built by ITSO-SVR to complete the nonlinear modelling of SRM. Finally, the effectiveness of the proposed method is verified. The preprocessing method is verified to reduce the modeling difficulty. Besides, ITSO-SVR facilitates the swift and efficient modeling without any pre-storge or complex calculations. The experiments under the CCC and APC algorithms indicate that the established model exhibits high accuracy, fast speed and strong generalization capability.
AbstractList	This article proposes a machine learning method to build the model of a switched reluctance motor (SRM) using few preprocessed flux linkage data. Firstly, the improved torque balance method is used to obtain the accurate flux linkage data without redundant experiments. Secondly, two special data preprocessing steps are proposed, which are nonlinear preprocessing and angle mapping, respectively. The first step provides beneficial nonlinearity for algorithms, and the second step improves the linearity of flux linkage at small angles by the proposed mapping function. Thirdly, support vector regression algorithm optimized by the improved tuna swarm algorithm (ITSO-SVR) is employed to establish the flux linkage model. Based on the flux linkage model, the current and torque models are easily built by ITSO-SVR to complete the nonlinear modelling of SRM. Finally, the effectiveness of the proposed method is verified. The preprocessing method is verified to reduce the modeling difficulty. Besides, ITSO-SVR facilitates the swift and efficient modeling without any pre-storge or complex calculations. The experiments under the CCC and APC algorithms indicate that the established model exhibits high accuracy, fast speed and strong generalization capability. This article proposes a machine learning method to build the model of a switched reluctance motor (SRM) using few preprocessed fl ux linkage data. Firstly, the improved torque balance method is used to obtain the accurate fl ux linkage data without redundant experiments. Secondly, two special data preprocessing steps are proposed, which are nonlinear preprocessing and angle mapping, respectively. The fi rst step provides benefi cial nonlinearity for algorithms, and the second step improves the linearity of fl ux linkage at small angles by the proposed mapping function. Thirdly, support vector regression algorithm optimized by the improved tuna swarm algorithm (ITSO-SVR) is employed to establish the fl ux linkage model. Based on the fl ux linkage model, the current and torque models are easily built by ITSO-SVR to complete the nonlinear modelling of SRM. Finally, the eff ectiveness of the proposed method is verifi ed. The preprocessing method is verifi ed to reduce the modeling diffi culty. Besides, ITSO-SVR facilitates the swift and effi cient modeling without any pre-storge or complex calculations. The experiments under the CCC and APC algorithms indicate that the established model exhibits high accuracy, fast speed and strong generalization capability. KCI Citation Count: 0
Author	Jing, Zhe Ren, Ping Zhu, Jingwei zhao, Yan
Author_xml	– sequence: 1 givenname: Yan surname: zhao fullname: zhao, Yan organization: Department of Marine Electrical Engineering, Dalian Maritime University – sequence: 2 givenname: Jingwei orcidid: 0000-0002-1737-3108 surname: Zhu fullname: Zhu, Jingwei email: zjwdl@dlmu.edu.cn organization: Department of Marine Electrical Engineering, Dalian Maritime University – sequence: 3 givenname: Ping surname: Ren fullname: Ren, Ping organization: Department of Marine Electrical Engineering, Dalian Maritime University – sequence: 4 givenname: Zhe surname: Jing fullname: Jing, Zhe organization: Department of Marine Electrical Engineering, Dalian Maritime University
BackLink	https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003212876$$DAccess content in National Research Foundation of Korea (NRF)
BookMark	eNp9kU9PGzEQxa0KpAbaL9CTJW5IC2N7nd0cw58UpKAiSs_WxB4nS4IN9ka03x4ni8Sth9GMZn7z9KR3xA5CDMTYDwFnAqA5z7Vsla5A7kqOddV-YSMJE1U1tVQHbCQmTTkJkF_ZUc5PAGMBWo3Y85TfoV11gficMIUuLPlddLTZD9SvouM-Jv77revtihx_oM3W9hgsFa6PKfMLzGUfA5_RG79P9JKipbzbzTbbv3zehTUuiV9hj9_YocdNpu8f_Zj9mV0_Xt5U818_by-n88oqaPrKWaQGW1r4hXfW1ZOx8ILGC0BwUi40ohY1gCXtwNXohIPWT7z2AgnBtuqYnQ66IXmztp2J2O37Mpp1MtOHx1sjoJFKNqrAJwNcjL9uKffmKW5TKP6MklproUSjCyUHyqaYcyJvXlL3jOlfETK7DMyQgSkZmH0GZudDDU-5wGFJ6VP6P1_vXViL-Q
Cites_doi	10.1109/TPEL.2024.3363027 10.1155/2021/9210050 10.1109/TMECH.2018.2803148 10.1109/TEC.2014.2343962 10.1007/s42835-022-01350-6 10.1109/TIE.2024.3352142 10.1109/TEC.2016.2517924 10.1109/ACCESS.2018.2837111 10.1109/TEC.2022.3208831 10.1109/TPEL.2023.3349283 10.1109/IECON.2005.1569136 10.1109/TMAG.2021.3111521 10.1007/s00202-022-01726-x 10.1109/TIE.2015.2390147 10.1109/TIE.2022.3199860 10.1109/TEC.2023.3307574 10.1016/j.measurement.2023.112447 10.1109/TIM.2007.904562 10.1007/s42835-023-01703-9 10.3390/app8040537
ContentType	Journal Article
Copyright	The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2025.
Copyright_xml	– notice: The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2025.
DBID	AAYXX CITATION ACYCR
DOI	10.1007/s42835-025-02265-8
DatabaseName	CrossRef Korean Citation Index
DatabaseTitle	CrossRef
DatabaseTitleList
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2093-7423
EndPage	3456
ExternalDocumentID	oai_kci_go_kr_ARTI_10723273 10_1007_s42835_025_02265_8
GroupedDBID	-~X 0R~ 2WC 406 9ZL AACDK AAHNG AAJBT AASML AATNV AAUYE AAYYP ABAKF ABBRH ABDBE ABECU ABFSG ABFTV ABJNI ABKCH ABMQK ABTEG ABTKH ABTMW ACAOD ACDTI ACHSB ACOKC ACPIV ACSTC ACZOJ ADKNI ADTPH ADURQ ADYFF AEFQL AEMSY AENEX AESKC AEZWR AFBBN AFDZB AFHIU AFOHR AFQWF AGDGC AGMZJ AGQEE AHPBZ AHWEU AIGIU AILAN AITGF AIXLP AJZVZ ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF ATHPR AXYYD AYFIA BGNMA CSCUP DBRKI DPUIP EBLON EBS EJD FIGPU FNLPD GGCAI GW5 IKXTQ IWAJR JDI JZLTJ KOV KVFHK LLZTM M4Y NPVJJ NQJWS NU0 OK1 PT4 ROL RSV SJYHP SNE SNPRN SOHCF SOJ SRMVM SSLCW TDB UOJIU UTJUX VEKWB VFIZW ZMTXR AAYXX ABJCF ABRTQ AEUYN AFFHD AFKRA ARAPS BENPR BGLVJ CCPQU CITATION HCIFZ M7S PHGZM PHGZT PQGLB PTHSS ACYCR
ID	FETCH-LOGICAL-c307t-dcae7a8ebfbfdcd4961f1e6b0a0d22b5aa51400ce5d0d4ad1d08f9f5f1aea0c83
IEDL.DBID	RSV
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001483459800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1975-0102
IngestDate	Sun Jun 29 03:11:35 EDT 2025 Sun Nov 09 08:21:47 EST 2025 Sat Nov 29 07:49:50 EST 2025 Wed Jun 25 02:38:14 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	5
Keywords	Nonlinear modeling Switched reluctance motor Data preprocessing Improved tuna swarm algorithm Support vector regression algorithm
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c307t-dcae7a8ebfbfdcd4961f1e6b0a0d22b5aa51400ce5d0d4ad1d08f9f5f1aea0c83
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-1737-3108
PQID	3255513175
PQPubID	7435074
PageCount	12
ParticipantIDs	nrf_kci_oai_kci_go_kr_ARTI_10723273 proquest_journals_3255513175 crossref_primary_10_1007_s42835_025_02265_8 springer_journals_10_1007_s42835_025_02265_8
PublicationCentury	2000
PublicationDate	20250700 2025-07-00 20250701 2025-07
PublicationDateYYYYMMDD	2025-07-01
PublicationDate_xml	– month: 7 year: 2025 text: 20250700
PublicationDecade	2020
PublicationPlace	Singapore
PublicationPlace_xml	– name: Singapore – name: Seoul
PublicationTitle	Journal of electrical engineering & technology
PublicationTitleAbbrev	J. Electr. Eng. Technol
PublicationYear	2025
Publisher	Springer Nature Singapore Springer Nature B.V 대한전기학회
Publisher_xml	– name: Springer Nature Singapore – name: Springer Nature B.V – name: 대한전기학회
References	JW Gan (2265_CR6) 2018; 6 L Xie (2265_CR21) 2021; 2021 2265_CR15 P Ren (2265_CR3) 2024; 39 X Sun (2265_CR1) 2018; 23 J Liu (2265_CR11) 2021; 57 M Shi (2265_CR14) 2023; 70 2265_CR2 Z Wang (2265_CR8) 2023; 38 M Shi (2265_CR13) 2023; 208 S Song (2265_CR16) 2016; 31 S Song (2265_CR10) 2015; 62 A Chiba (2265_CR7) 2015; 30 M Aydemir (2265_CR18) 2023; 105 2265_CR20 X Li (2265_CR5) 2023; 18 Y Cai (2265_CR17) 2018; 23 J Liu (2265_CR4) 2024; 19 X Sun (2265_CR19) 2024; 39 AD Cheok (2265_CR9) 2007; 56 R Rocca (2265_CR12) 2024; 39
References_xml	– volume: 39 start-page: 5298 issue: 5 year: 2024 ident: 2265_CR19 publication-title: IEEE Trans Power Electron doi: 10.1109/TPEL.2024.3363027 – volume: 2021 start-page: 22 year: 2021 ident: 2265_CR21 publication-title: Comput Intell Neurosci doi: 10.1155/2021/9210050 – volume: 23 start-page: 747 issue: 2 year: 2018 ident: 2265_CR1 publication-title: IEEE ASME Trans Mechatron doi: 10.1109/TMECH.2018.2803148 – volume: 30 start-page: 175 issue: 1 year: 2015 ident: 2265_CR7 publication-title: IEEE Trans Energy Convers doi: 10.1109/TEC.2014.2343962 – volume: 18 start-page: 981 issue: 2 year: 2023 ident: 2265_CR5 publication-title: J Electr Eng Technol doi: 10.1007/s42835-022-01350-6 – ident: 2265_CR2 doi: 10.1109/TIE.2024.3352142 – volume: 31 start-page: 424 issue: 2 year: 2016 ident: 2265_CR16 publication-title: IEEE Trans Energy Convers doi: 10.1109/TEC.2016.2517924 – volume: 6 start-page: 31430 year: 2018 ident: 2265_CR6 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2837111 – volume: 38 start-page: 122 issue: 1 year: 2023 ident: 2265_CR8 publication-title: IEEE Trans Energy Convers doi: 10.1109/TEC.2022.3208831 – volume: 39 start-page: 4567 issue: 4 year: 2024 ident: 2265_CR3 publication-title: IEEE Trans Power Electron doi: 10.1109/TPEL.2023.3349283 – ident: 2265_CR20 doi: 10.1109/IECON.2005.1569136 – volume: 57 start-page: 1 issue: 11 year: 2021 ident: 2265_CR11 publication-title: IEEE Trans Magn doi: 10.1109/TMAG.2021.3111521 – volume: 105 start-page: 1223 issue: 2 year: 2023 ident: 2265_CR18 publication-title: Electr Eng doi: 10.1007/s00202-022-01726-x – volume: 62 start-page: 4105 issue: 7 year: 2015 ident: 2265_CR10 publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2015.2390147 – volume: 70 start-page: 6099 issue: 6 year: 2023 ident: 2265_CR14 publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2022.3199860 – volume: 39 start-page: 734 issue: 1 year: 2024 ident: 2265_CR12 publication-title: IEEE Trans Energy Convers doi: 10.1109/TEC.2023.3307574 – volume: 208 start-page: 112447 year: 2023 ident: 2265_CR13 publication-title: Measurement doi: 10.1016/j.measurement.2023.112447 – volume: 23 start-page: 2762 issue: 6 year: 2018 ident: 2265_CR17 publication-title: IEEE ASME Trans Mechatron – volume: 56 start-page: 2245 issue: 6 year: 2007 ident: 2265_CR9 publication-title: IEEE Trans Instrum Meas doi: 10.1109/TIM.2007.904562 – volume: 19 start-page: 2295 issue: 4 year: 2024 ident: 2265_CR4 publication-title: J Electr Eng Technol doi: 10.1007/s42835-023-01703-9 – ident: 2265_CR15 doi: 10.3390/app8040537
SSID	ssj0061053
Score	2.3411195
Snippet	This article proposes a machine learning method to build the model of a switched reluctance motor (SRM) using few preprocessed flux linkage data. Firstly, the... This article proposes a machine learning method to build the model of a switched reluctance motor (SRM) using few preprocessed fl ux linkage data. Firstly, the...
SourceID	nrf proquest crossref springer
SourceType	Open Website Aggregation Database Index Database Publisher
StartPage	3445
SubjectTerms	Electrical Engineering Electrical Machines and Networks Electronics and Microelectronics Engineering Instrumentation Original Article Power Electronics 전기공학
Title	A Machine Learning Modeling Method for Switched Reluctance Motors Based on Few Preprocessed Flux Linkage Data
URI	https://link.springer.com/article/10.1007/s42835-025-02265-8 https://www.proquest.com/docview/3255513175 https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003212876
Volume	20
WOSCitedRecordID	wos001483459800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
ispartofPNX	Journal of Electrical Engineering & Technology, 2025, 20(5), , pp.3445-3456
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 2093-7423 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0061053 issn: 1975-0102 databaseCode: RSV dateStart: 20190101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3JTsMwEB2xHeDAjigUZAluEMmpsx7LUsEBVLWAerMc26mqlhS1KeXzGZukUKQe4JQocRb5zXie5fEbgHMdM-V5TCACkjpeguOgkDH6FUtipVwapYGwxSbCx8eo04mbxaawcZntXi5J2pF6ttnNSoM5pvwqxp3Ad6JlWMVwFxl3bLVfyvEX-YDVnnTj0KSlmfydo8XvmAtHy9konWOavxZHbcxpbP3vb7dhs-CYpP5lFDuwpLNd2PihPLgHr3XyYNMoNSkUVrvElEUb2BNbVZognSXtac_AqkhLDyYyNyaC7UyFHnKF8U-RYUYaekqaRhzTbjnAa43B5IOYSS6OVeRG5GIfnhu3T9d3TlF5wZHo87mjpNChiHSSJqmSyosDN3V1kFBBVa2W-EIgz6JUal9R5QnlKgQ1Tv3UFVpQGbEDWMmGmT4EooTvB7H2NNMUbYIKxgTzoiSQYYLsSlXgogSAv30JbPCZlLLtRI6dyG0n8qgCZ4gR78seN7rY5tgd8v6II_u_x4dCJIghq0C1xJAXLjnmDCdPvmvoUgUuS8y-by_-5tHfmh_Des3CblJ6q7CSjyb6BNbke94bj06tqX4CmPTi-g
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3JTsMwEB1RQAIO7IiWzRLcIJJTZz2yVSBKhdjUm-XYTlVRUtSmlM9nbJKySBzglChxFvmNZ57l8RuAAx0z5XlMIAKSOl6CflDIGMcVS2KlXBqlgbDFJsJWK2q345tiU9iwzHYvlyStp55sdrPSYI4pv4pxJ_CdqAIzHkYsk8h3e_dY-l_kA1Z70o1Dk5Zm8ndqv7_jWziqZIP0G9P8sThqY05j6X9_uwyLBcckxx9GsQJTOluFhS_Kg2vwfEyubRqlJoXCaoeYsmg9e2KrShOks-Ru3DWwKnKreyOZGxPBdqZCDznB-KdIPyMNPSY3RhzTbjnAa43e6I2YSS76KnImcrEOD43z-9MLp6i84Egc87mjpNChiHSSJqmSyosDN3V1kFBBVb2e-EIgz6JUal9R5QnlKgQ1Tv3UFVpQGbENmM76md4EooTvB7H2NNMUbYIKxgTzoiSQYYLsSlXhsASAv3wIbPCJlLLtRI6dyG0n8qgK-4gRf5JdbnSxzbHT508Djuz_Eh8KkSCGrArbJYa8GJJDznDy5LuGLlXhqMTs8_bv36z9rfkezF3cXzd587J1tQXzdWsCJr13G6bzwUjvwKx8zbvDwa4123dE-OXe
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1ZT8MwDLa4hOCBGzHOSPAG1dKl5yNXBQKmiQHiLUqTdJo2OjQ64OfjZCsMJB4QT63a9FDs2J8V-zPAgY6Z8jwmUAKSOl6KdlDIGNcVS2OlXBplgbDNJsJ6PXp8jBtjVfw2273ckhzWNBiWpryoPqus-ln4ZmnCHNOKFX1Q4DvRJEx7pmmQidebD6UtRmxgeSjdODQpaiaXZ_P3d3xzTZN5P_uGOn9slFr_kyz-_8-XYGGEPcnxUFmWYULnKzA_xki4Ck_H5MamV2oyYl5tEdMurWtPbLdpgjCXNN_aRtyK3OruQBZGdXCc6dxDTtAvKtLLSaLfSMOQZtpSBLyWdAfvxAS_aMPImSjEGtwn53enF86oI4Mj0RYUjpJChyLSaZZmSiovDtzM1UFKBVW1WuoLgfiLUql9RZUnlKtQ2HHmZ67QgsqIrcNU3sv1BhAlfD-ItaeZpqgrVDAmmBelgQxTRF2qAoelMPjzkHiDf1Is20nkOIncTiKPKrCP8uId2eaGL9scWz3e6XOMCi7xoRCBY8gqsF3Kk4-W6gtnGFT5roFRFTgq5fd1-_dvbv5t-B7MNs4Sfn1Zv9qCuZrVAJP1uw1TRX-gd2BGvhbtl_6u1eAPMY_uwg
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Machine+Learning+Modeling+Method+for+Switched+Reluctance+Motors+Based+on+Few+Preprocessed+Flux+Linkage+Data&rft.jtitle=Journal+of+electrical+engineering+%26+technology&rft.au=zhao%2C+Yan&rft.au=Zhu%2C+Jingwei&rft.au=Ren%2C+Ping&rft.au=Jing%2C+Zhe&rft.date=2025-07-01&rft.pub=Springer+Nature+Singapore&rft.issn=1975-0102&rft.eissn=2093-7423&rft.volume=20&rft.issue=5&rft.spage=3445&rft.epage=3456&rft_id=info:doi/10.1007%2Fs42835-025-02265-8&rft.externalDocID=10_1007_s42835_025_02265_8
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1975-0102&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1975-0102&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1975-0102&client=summon