Predicting the variability of thermal runaway behavior of Li-ion cells with little to no calorimetry data

Predicting the variability of thermal runaway behavior of Li-ion cells with little to no calorimetry data | 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 Predicting the variability of thermal runaway behavior of Li-ion cells with little to no calorimetry data Donal Finegan, Karina Masalkovaite, Paul Gasper This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3937313/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Sep, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Accurate measurement of the variability of thermal runaway behavior of lithium-ion cells is critical for designing safe battery systems. However, experimentally determining such variability is challenging, expensive, and time-consuming. Here, we utilize a transfer learning approach to accurately estimate the variability of heat output during thermal runaway using only ejected mass measurements and cell metadata, leveraging 139 calorimetry measurements on commercial lithium-ion cells available from the open-access Battery Failure Databank. We show that the distribution of heat output, including outliers, can be predicted accurately and with high confidence for new cell types using just 0 to 5 calorimetry measurements by leveraging behaviors learned from the Battery Failure Databank. Fractional heat ejection from the positive vent, cell body, and negative vent are also accurately predicted. We demonstrate that by using low cost and fast measurements, we can predict the variability in thermal behaviors of cells, thus accelerating critical safety characterization efforts. Physical sciences/Engineering/Chemical engineering Physical sciences/Energy science and technology/Energy storage/Batteries Physical sciences/Engineering/Mechanical engineering Full Text Additional Declarations There is NO Competing Interest. Supplementary Data is not available with this version. Cite Share Download PDF Status: Published Journal Publication published 27 Sep, 2024 Read the published version in Nature Communications → 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. 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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-3937313","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":274123745,"identity":"8256594a-a05c-4e49-b52c-e50bdee71810","order_by":0,"name":"Donal Finegan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBACexDxgOE/D4RbAcTsBLQYNgCJBIYDUC1ngJiZgBaDAxAtEB5jGzFajvce/ADUImPO3n7xc+G8O/n8zAzMLz624dFy5lyyRALDHx7LnjPF0jO3PbOc2czAZjkTn5YbOQYSIL8AGQnSvNsOGxgcZmAz5jmDV4vxD7CW+2+Sf/POOWxgT4QWM6gt7MekeRuAtgD98pinArcWw54zZhYJBkAtZ3LYrHmOHTaQOMzYxjgDjxZ79h7jGx8qDtgbHD/++DZPzWED/vbmwx8+GODWAnUeiOCBKWNskyCkAQrYH8BYzB+I1DIKRsEoGAUjAwAAvLpTC/SnUNAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-4633-560X","institution":"National Renewable Energy Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Donal","middleName":"","lastName":"Finegan","suffix":""},{"id":274123746,"identity":"d10f584e-c459-40a5-972d-225343a5edc8","order_by":1,"name":"Karina Masalkovaite","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Karina","middleName":"","lastName":"Masalkovaite","suffix":""},{"id":274123747,"identity":"d02dec4b-1ab8-4774-9f95-310bb935d114","order_by":2,"name":"Paul Gasper","email":"","orcid":"https://orcid.org/0000-0001-8834-9458","institution":"National Renewable Energy Lab","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Gasper","suffix":""}],"badges":[],"createdAt":"2024-02-07 15:35:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3937313/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3937313/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-024-52653-3","type":"published","date":"2024-09-27T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65485006,"identity":"4b3570d3-3179-4ec0-b1b0-9180a43be632","added_by":"auto","created_at":"2024-09-28 07:09:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2948677,"visible":true,"origin":"","legend":"","description":"","filename":"MasalkovaitePredictingThermalRunawayFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3937313/v1_covered_1cf1456b-dc49-46cb-bd6c-9126ba39f6e6.pdf"}],"financialInterests":"\u003cp\u003eThere is \u003cstrong\u003eNO\u003c/strong\u003e Competing Interest.\u003c/p\u003e\n\u003cp\u003eSupplementary Data is not available with this version.\u003c/p\u003e","formattedTitle":"Predicting the variability of thermal runaway behavior of Li-ion cells with little to no calorimetry data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3937313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3937313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate measurement of the variability of thermal runaway behavior of lithium-ion cells is critical for designing safe battery systems. 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