Multimodal Data-Driven Abnormal Condition Detection for Energy Storage Batteries Using EAA-Informer

Multimodal Data-Driven Abnormal Condition Detection for Energy Storage Batteries Using EAA-Informer | 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 Research Article Multimodal Data-Driven Abnormal Condition Detection for Energy Storage Batteries Using EAA-Informer Jun Xie, Zezhong Sun, Yan Li, Erik Cambria, Xianxun Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6502419/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 To enhance the operational safety of energy storage batteries under complex conditions, this study proposes a multimodal data-driven abnormal condition identification method based on Enhanced Attention Allocation (EAA) Informer.First, an analysis of thermal experimental data under various abnormal conditions reveals that temperature changes during the early stages are often subtle. As a result, relying on a single temperature parameter is insufficient to accurately reflect the battery state. This requires the integration of multiple parameters into a joint modeling framework to improve identification accuracy. Subsequently, a multimodal temporal feature extraction network based on LSTM-MFM is constructed. This network enables deep modeling of multi-source data—such as voltage, current, temperature, and state of charge—and facilitates cross-modal correlation learning.To further enhance the model's ability to capture both critical time steps and long-range temporal dependencies, an EAA mechanism is introduced for feature optimization. This is followed by an improved Informer network to perform temporal modeling and abnormal condition identification. Experimental results show that the proposed method accurately identifies abnormalities with low false rates, highlighting its potential for energy storage system safety monitoring. Energy Storage Batteries Multimodal Data Fusion LSTM-MFM Time-Series Analysis. Full Text Additional Declarations No competing interests reported. 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-6502419","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452513558,"identity":"37f68248-ecbc-4b80-bbcb-472aa145b2fc","order_by":0,"name":"Jun Xie","email":"","orcid":"","institution":"North China Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Xie","suffix":""},{"id":452513559,"identity":"857cb434-b6c6-4c5b-8fb1-bbddd4d8d0b3","order_by":1,"name":"Zezhong Sun","email":"","orcid":"","institution":"North China Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Zezhong","middleName":"","lastName":"Sun","suffix":""},{"id":452513560,"identity":"dc0314cf-fb31-42e8-ace8-f84b7c6442cf","order_by":2,"name":"Yan Li","email":"","orcid":"","institution":"North China Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""},{"id":452513561,"identity":"3fd0c9f3-fd04-419d-8519-8342359bce9f","order_by":3,"name":"Erik Cambria","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"","lastName":"Cambria","suffix":""},{"id":452513562,"identity":"6f970d85-6211-4a43-b91d-74b76e7659fb","order_by":4,"name":"Xianxun Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACCSBOALOYDzCD6QPEa2FLIEELBPAYEKdFfnaPmcSDmjuJG273fP5c2MYgx3cjgfFzAR4tjHPOGBskHHtmbHDn7DbpmW0MxpI3EpilZ+DRwiyRY/ggge2wnMGN3G3MvG0MiRtuJLAx8+DRwiaRY3Ag4d9hHoMbOY8/A7XUE9TCA7IlsQ1kSw6DNFBLggEhLRISacUGiX2HgV5IM5PmOSdhOPPMw2ZpfFrkZyRvk/zx7XBi343kx595ymzk+Y4nH/yMTwuGrUDM2ECChlEwCkbBKBgF2AAArbdJ9U35KV4AAAAASUVORK5CYII=","orcid":"","institution":"Shanghai University","correspondingAuthor":true,"prefix":"","firstName":"Xianxun","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-04-22 09:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6502419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6502419/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96454311,"identity":"ef42911d-aeed-4989-b671-f9ff5817c825","added_by":"auto","created_at":"2025-11-21 10:02:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4977348,"visible":true,"origin":"","legend":"","description":"","filename":"Main2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6502419/v1_covered_27d891e0-9279-4e45-b76d-053db2dea28c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multimodal Data-Driven Abnormal Condition Detection for Energy Storage Batteries Using EAA-Informer","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":"Energy Storage Batteries, Multimodal Data Fusion, LSTM-MFM, Time-Series Analysis.","lastPublishedDoi":"10.21203/rs.3.rs-6502419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6502419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To enhance the operational safety of energy storage batteries under complex conditions, this study proposes a multimodal data-driven abnormal condition identification method based on Enhanced Attention Allocation (EAA) Informer.First, an analysis of thermal experimental data under various abnormal conditions reveals that temperature changes during the early stages are often subtle. 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