Multi-scale nonlinear temporal-aware enhancement network for lithium-ion battery life prediction

Multi-scale nonlinear temporal-aware enhancement network for lithium-ion battery life prediction | 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 Multi-scale nonlinear temporal-aware enhancement network for lithium-ion battery life prediction Lu Chen, Zhibo Liu, Yongming Liu, Jiajia Ni, Zhuanzhe Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8375006/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 Accurate prediction of lithium-ion battery lifetime is crucial for the health management of electric vehicle batteries. However, current prediction models based on machine learning or traditional neural networks are often limited in their ability to effectively capture global dependencies within long-term sequential data and typically underperform when handling local fluctuations that occur during battery degradation, which consequently constrains their prediction accuracy. To address these limitations, an innovative battery lifetime prediction model named multi-scale nonlinear temporal-aware enhancement network (MNTA-Net) is proposed. This model incorporates a Multi-scale Nonlinear Feature Extraction (MNFE) module. Traditional pooling operations are abandoned in this module in favor of employing non-linear activation functions to preserve detailed features, thereby enabling the effective capture of mutation points and local fluctuations within the capacity sequence. Simultaneously, a Temporal-Aware Enhancement (TAE) module is designed to deeply integrate underlying temporal networks with an attention mechanism. This integration is intended to achieve precise modeling of long-term dependencies and facilitate the dynamic perception of key cycle nodes. Experimental results obtained from several public datasets demonstrate that the proposed method significantly outperforms mainstream benchmark models across evaluation metrics such as RMSE, MAE, and R². These findings validate the effectiveness and generalizability of the multi-scale nonlinear feature extraction and temporal enhancement mechanisms in improving the accuracy of battery lifetime prediction. lithium-ion battery MNFE TAE lifetime prediction ReLU 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-8375006","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592147824,"identity":"3b93a2a9-5335-4014-abb4-984ea4cac668","order_by":0,"name":"Lu Chen","email":"","orcid":"","institution":"Anhui University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Chen","suffix":""},{"id":592147826,"identity":"8d7da0d1-796b-469c-89f4-3f6886740522","order_by":1,"name":"Zhibo Liu","email":"","orcid":"","institution":"Anhui University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Zhibo","middleName":"","lastName":"Liu","suffix":""},{"id":592147827,"identity":"26489ea3-2441-47b0-9e7e-c9981decd887","order_by":2,"name":"Yongming Liu","email":"","orcid":"","institution":"Anhui University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Yongming","middleName":"","lastName":"Liu","suffix":""},{"id":592147828,"identity":"edf4229f-1a81-4a1e-8dde-3d4531063d32","order_by":3,"name":"Jiajia Ni","email":"","orcid":"","institution":"Anhui University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Jiajia","middleName":"","lastName":"Ni","suffix":""},{"id":592147829,"identity":"03ffa066-a7bb-4cda-8eeb-d0f9bdd1563e","order_by":4,"name":"Zhuanzhe Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYDACCTB5gMH+eP/HB4wNYJ4BcVoYzhwwNiBRy40EMwmitMjP7jH8XPDnjhxjQ0Jaxc8ddxIb2Ju3STDU3MGphXHOGWPpGTzPjJkZDhy72XvmWWIDz7EyCYZjz3BqYZbIMZDmkTic2MbY2HaDt+1wYoNEDsiFh3FqYZPIMf7NY3C4voeZma3wL0iL/Bv8WniAZkrzJBxOkGBjY2OG2MKDX4uERFqZNc+Bw4YbeHiYpWXbnhm38aQVWyQcw61Ffkby5ts8fw7LG8i/Yfz4tu2ObD/74Y03PtTg1oIODjCwgagEojWA43QUjIJRMApGARoAAAt5VtdAOI5hAAAAAElFTkSuQmCC","orcid":"","institution":"Anhui University of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Zhuanzhe","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-12-16 10:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8375006/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8375006/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104333747,"identity":"7c6393aa-6b69-4d3d-842d-260936a979cf","added_by":"auto","created_at":"2026-03-10 15:26:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1135784,"visible":true,"origin":"","legend":"","description":"","filename":"LithiumBatteryLifetimePredictionBasedonaNovelHybridModelofMNFETAE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8375006/v1_covered_2e4f7503-ad4c-431e-91f6-a8c9d74214f2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-scale nonlinear temporal-aware enhancement network for lithium-ion battery life prediction","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":"lithium-ion battery, MNFE, TAE, lifetime prediction, ReLU","lastPublishedDoi":"10.21203/rs.3.rs-8375006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8375006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate prediction of lithium-ion battery lifetime is crucial for the health management of electric vehicle batteries. However, current prediction models based on machine learning or traditional neural networks are often limited in their ability to effectively capture global dependencies within long-term sequential data and typically underperform when handling local fluctuations that occur during battery degradation, which consequently constrains their prediction accuracy. To address these limitations, an innovative battery lifetime prediction model named multi-scale nonlinear temporal-aware enhancement network (MNTA-Net) is proposed. This model incorporates a Multi-scale Nonlinear Feature Extraction (MNFE) module. Traditional pooling operations are abandoned in this module in favor of employing non-linear activation functions to preserve detailed features, thereby enabling the effective capture of mutation points and local fluctuations within the capacity sequence. Simultaneously, a Temporal-Aware Enhancement (TAE) module is designed to deeply integrate underlying temporal networks with an attention mechanism. This integration is intended to achieve precise modeling of long-term dependencies and facilitate the dynamic perception of key cycle nodes. Experimental results obtained from several public datasets demonstrate that the proposed method significantly outperforms mainstream benchmark models across evaluation metrics such as RMSE, MAE, and R\u0026sup2;. These findings validate the effectiveness and generalizability of the multi-scale nonlinear feature extraction and temporal enhancement mechanisms in improving the accuracy of battery lifetime prediction.\u003c/p\u003e","manuscriptTitle":"Multi-scale nonlinear temporal-aware enhancement network for lithium-ion battery life prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 04:59:21","doi":"10.21203/rs.3.rs-8375006/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":"1bc20802-01b6-4db9-aaf7-836682db55a6","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T15:25:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 04:59:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8375006","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8375006","identity":"rs-8375006","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}