Data-driven methodology for optimal Lithium-ion battery RUL prediction

Data-driven methodology for optimal Lithium-ion battery RUL 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 Data-driven methodology for optimal Lithium-ion battery RUL prediction Daniel Puente, Jon Amelibia, Iker Cumplido, Aitor Hernandez, Iñigo Ugarte, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2635321/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 Lithium-ion batteries have emerged as a prevalent power source in a variety of industries, including the electric vehicle sector due to their higher energy density and low self-discharge. With the use and passage of time, batteries degrade and eventually die, endangering the integrity of the objects they power. The ability to accurately predict the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for optimizing their utilization and ensuring their safe operation. For this purpose, a deep learning-based approach trained on the widely used Oxford battery degradation dataset with the help of Generative Adversarial Networks (GANs) has been implemented. The designed network consists of a Long-Short-Term Memory (LSTM) architecture with the implementation of a stratification strategy and a custom loss function. The illustrative results show that the suggested approach can produce adaptable and reliable predictions of the RUL. Deep Learning Lithium-ion battery RUL prediction LSTM GAN 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-2635321","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":181684593,"identity":"486abb25-5b3d-4077-9724-e750d8f72277","order_by":0,"name":"Daniel Puente","email":"data:image/png;base64,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","orcid":"","institution":"Mondragon University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Puente","suffix":""},{"id":181684594,"identity":"9240c830-887f-40da-972f-3036ef1c3345","order_by":1,"name":"Jon Amelibia","email":"","orcid":"","institution":"Mondragon University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jon","middleName":"","lastName":"Amelibia","suffix":""},{"id":181684596,"identity":"ec82d114-73dd-4558-9fb0-c7563d49e8f1","order_by":2,"name":"Iker Cumplido","email":"","orcid":"","institution":"Mondragon University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Iker","middleName":"","lastName":"Cumplido","suffix":""},{"id":181684598,"identity":"114060fb-3820-4303-8f14-9920558d3022","order_by":3,"name":"Aitor Hernandez","email":"","orcid":"","institution":"Mondragon University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aitor","middleName":"","lastName":"Hernandez","suffix":""},{"id":181684600,"identity":"852ff481-b6f1-4cb5-9e31-9428d7822460","order_by":4,"name":"Iñigo Ugarte","email":"","orcid":"","institution":"Mondragon University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Iñigo","middleName":"","lastName":"Ugarte","suffix":""},{"id":181684601,"identity":"6ea9a5f4-01a0-492c-bea0-f6cd64f2d5ae","order_by":5,"name":"Aitor Duo","email":"","orcid":"","institution":"Mondragon University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aitor","middleName":"","lastName":"Duo","suffix":""}],"badges":[],"createdAt":"2023-02-27 18:14:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2635321/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2635321/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34702893,"identity":"471b3052-d206-41c6-8192-39f42f4549b8","added_by":"auto","created_at":"2023-03-23 11:00:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":939165,"visible":true,"origin":"","legend":"","description":"","filename":"DatadrivenmethodologyforoptimalLithiumionbatteryRULprediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2635321/v1_covered.pdf"},{"id":34142568,"identity":"ee1c0ee1-6d3f-4f57-9450-383a0d6fef6b","added_by":"auto","created_at":"2023-03-13 07:19:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":976787,"visible":true,"origin":"","legend":"","description":"","filename":"DatadrivenmethodologyforoptimalLithiumionbatteryRULprediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2635321/v1/28059e53dc0006f2ede117e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data-driven methodology for optimal Lithium-ion battery RUL 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":"Deep Learning, Lithium-ion battery, RUL prediction, LSTM, GAN","lastPublishedDoi":"10.21203/rs.3.rs-2635321/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2635321/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Lithium-ion batteries have emerged as a prevalent power source in a variety of industries, including the electric vehicle sector due to their higher energy density and low self-discharge. With the use and passage of time, batteries degrade and eventually die, endangering the integrity of the objects they power. The ability to accurately predict the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for optimizing their utilization and ensuring their safe operation. For this purpose, a deep learning-based approach trained on the widely used Oxford battery degradation dataset with the help of Generative Adversarial Networks (GANs) has been implemented. The designed network consists of a Long-Short-Term Memory (LSTM) architecture with the implementation of a stratification strategy and a custom loss function. The illustrative results show that the suggested approach can produce adaptable and reliable predictions of the RUL.","manuscriptTitle":"Data-driven methodology for optimal Lithium-ion battery RUL prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-13 07:19:00","doi":"10.21203/rs.3.rs-2635321/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":"7772dbc8-adf0-49d9-8e19-7be50273ceb1","owner":[],"postedDate":"March 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-29T13:59:11+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-13 07:19:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2635321","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2635321","identity":"rs-2635321","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}