Efficient Data-Driven Modeling of Core Loss in Magnetic Materials for Power Electronics Systems

Efficient Data-Driven Modeling of Core Loss in Magnetic Materials for Power Electronics Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Efficient Data-Driven Modeling of Core Loss in Magnetic Materials for Power Electronics Systems Junqi He, Siyang Li, Hao Sheng, Rui Gao, Ying Luo, Yiming Gai, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7660717/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The accurate modeling and prediction of magnetic core losses are of great significance in the design of power electronic systems. Although traditional empirical equations (such as Steinmetz equation and its improved forms) are easy to calculate, their prediction accuracy is limited under multi material, multi temperature, and multi waveform conditions, making it difficult to meet the needs of high-frequency and high-power density applications. With the development of artificial intelligence, data-driven methods are gradually becoming more optimal solutions. Traditional machine learning models can achieve high accuracy in static features, but still rely on manual feature engineering for complex waveform sequences, resulting in information loss. The introduction of deep learning methods provides a new path for this. This paper proposes a CNN-FCNN architecture Mixed Neural Network (MNN) that can simultaneously process discrete, continuous, and waveform sequence features, achieving unified prediction across materials, temperatures, and waveforms. Based on the MagNet database, the results show that MNN significantly outperforms MLP-LSTM in terms of prediction accuracy and generalization. Further combining XGBoost to construct a weighted hybrid model achieved the highest predictive performance (R2=0.997). Data driven method can break through the limitations of empirical equation and single model, and achieve high-precision general modeling of core loss. This method not only reduces the cost of repetitive modeling for a single material or specific operating conditions, but also provides new ideas for building universal power electronics design tools. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7660717","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":527696860,"identity":"f418ee95-2fcf-43b2-9335-c4e1454f8e74","order_by":0,"name":"Junqi He","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Junqi","middleName":"","lastName":"He","suffix":""},{"id":527696861,"identity":"4da341bc-a85d-4e00-aed4-9b723ee3393a","order_by":1,"name":"Siyang Li","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Siyang","middleName":"","lastName":"Li","suffix":""},{"id":527696862,"identity":"2f2e32f0-f4ad-4d9d-87e8-3bdb13d9bf2f","order_by":2,"name":"Hao Sheng","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Sheng","suffix":""},{"id":527696863,"identity":"61b26883-5812-441d-a079-8a03d586a189","order_by":3,"name":"Rui Gao","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Gao","suffix":""},{"id":527696864,"identity":"ba3d141b-7e55-4886-b264-ec99e3be7899","order_by":4,"name":"Ying Luo","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Luo","suffix":""},{"id":527696865,"identity":"2649868d-af3a-46e7-b906-146b7c3c78dd","order_by":5,"name":"Yiming Gai","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Gai","suffix":""},{"id":527696866,"identity":"c0e1efad-5fb1-4ca0-88d8-bbe1f7b88ac2","order_by":6,"name":"Xudong Li","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Xudong","middleName":"","lastName":"Li","suffix":""},{"id":527696867,"identity":"d702880b-13b0-435d-b259-681306f181a3","order_by":7,"name":"Yuyang Miao","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Yuyang","middleName":"","lastName":"Miao","suffix":""},{"id":527696868,"identity":"b1c6058b-718b-48de-9769-d4a884b4afae","order_by":8,"name":"Yutong Zhou","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Zhou","suffix":""},{"id":527696869,"identity":"8d1ba405-ea59-4241-ac9c-cc142180620d","order_by":9,"name":"Yikang Xu","email":"","orcid":"","institution":"Shanghai installation engineering group CO., LTD.","correspondingAuthor":false,"prefix":"","firstName":"Yikang","middleName":"","lastName":"Xu","suffix":""},{"id":527696870,"identity":"fbb2ac35-aa54-4f3a-9ab8-c5e90b124eff","order_by":10,"name":"Tianjun He","email":"","orcid":"","institution":"Shanghai installation engineering group CO., LTD.","correspondingAuthor":false,"prefix":"","firstName":"Tianjun","middleName":"","lastName":"He","suffix":""},{"id":527696871,"identity":"2a3e6078-1105-4cbf-a639-aa1355371e18","order_by":11,"name":"Jian Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYBACA4bDx6DMBKK1HEsjWQuPGYlazBnPfHvMU2PHwM+eY8DwcwcRWiwbzm435jmWzCDZ88aAsfcMMQ47cHabNA8bM4PBjRwDZsY2orSceSbN86+ewZ4ULWzSvG2HGQwkiNdyzNxwbt9xHokzzwoO9hKl5cbhZw/efKuW429P3vjgJzFaGCQOgCkeEHGAGA0MDPwNxKkbBaNgFIyCEQwAwi411eRwvDUAAAAASUVORK5CYII=","orcid":"","institution":"Beihang University","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-09-19 17:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7660717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7660717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93382080,"identity":"30bda7d8-1474-4d88-bfa3-f7413a7d5d8f","added_by":"auto","created_at":"2025-10-13 09:02:02","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12453,"visible":true,"origin":"","legend":"","description":"","filename":"1fcd920e214c4eaf8f866f1dae04d190.json","url":"https://assets-eu.researchsquare.com/files/rs-7660717/v1/a28f87287314f0a55d312a50.json"},{"id":101296677,"identity":"07fc08be-9993-4897-92e2-537adaaf2a2a","added_by":"auto","created_at":"2026-01-28 09:18:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2656770,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7660717/v1_covered_de463e73-d84b-4ac0-8a0b-aef332d839af.pdf"},{"id":93382081,"identity":"a4292ec4-2882-4404-a279-668873cf3788","added_by":"auto","created_at":"2025-10-13 09:02:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2414837,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7660717/v1/e7c3ce528db4bbf42d5bcb7b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Efficient Data-Driven Modeling of Core Loss in Magnetic Materials for Power Electronics Systems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7660717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7660717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The accurate modeling and prediction of magnetic core losses are of great significance in the design of power electronic systems. Although traditional empirical equations (such as Steinmetz equation and its improved forms) are easy to calculate, their prediction accuracy is limited under multi material, multi temperature, and multi waveform conditions, making it difficult to meet the needs of high-frequency and high-power density applications. With the development of artificial intelligence, data-driven methods are gradually becoming more optimal solutions. Traditional machine learning models can achieve high accuracy in static features, but still rely on manual feature engineering for complex waveform sequences, resulting in information loss. The introduction of deep learning methods provides a new path for this. This paper proposes a CNN-FCNN architecture Mixed Neural Network (MNN) that can simultaneously process discrete, continuous, and waveform sequence features, achieving unified prediction across materials, temperatures, and waveforms. Based on the MagNet database, the results show that MNN significantly outperforms MLP-LSTM in terms of prediction accuracy and generalization. Further combining XGBoost to construct a weighted hybrid model achieved the highest predictive performance (R2=0.997). Data driven method can break through the limitations of empirical equation and single model, and achieve high-precision general modeling of core loss. This method not only reduces the cost of repetitive modeling for a single material or specific operating conditions, but also provides new ideas for building universal power electronics design tools.","manuscriptTitle":"Efficient Data-Driven Modeling of Core Loss in Magnetic Materials for Power Electronics Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 09:01:58","doi":"10.21203/rs.3.rs-7660717/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dbada69d-96c6-41f4-b690-18e504b35265","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56093866,"name":"Physical sciences/Energy science and technology"},{"id":56093867,"name":"Physical sciences/Engineering"},{"id":56093868,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-01-27T14:11:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-13 09:01:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7660717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7660717","identity":"rs-7660717","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}