Remaining useful life prediction of lithium batteries based on jump- connected multi-scale CNN

Remaining useful life prediction of lithium batteries based on jump- connected multi-scale CNN | 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 Remaining useful life prediction of lithium batteries based on jump- connected multi-scale CNN Lin Sun, Xiaojie Huang, Jing Liu, Jing Song, Shimiao Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5015371/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In order to better utilize the feature information obtained by all convolutional layers in Convolutional Neural Networks (CNN), this paper proposes a lithium-ion battery remaining life prediction model based on jump connected multi-scale CNN. The proposed model takes the health factor of the battery as input, and uses the multi-scale CNN model based on jump connection to extract the local feature information and global feature information of the health factor of lithium-ion battery at different scales. Then, all the local feature information and global feature information are fused through the information fusion module, and finally the predicted remaining useful life is output. The experimental results show that the proposed method can predict the remaining useful life of lithium-ion batteries more accurately. Compared with the classical CNN method, Bi-LSTM method, EMD-LSTM method and VMD-GRU method, the root mean square error (ERMSE) of the proposed method is reduced by 75.7%, 78.3%, 83.8% and 77.8%, respectively. Mean absolute error (EMAE) decreased by 80.7%, 80.9%, 86.8%, 82.3%, and mean absolute percentage error (EMAPE) decreased by 81.0%, 82.2%, 87.0%, 83.1%, respectively. The model determination coefficient (R2) increased by 17.4%, 23.2%, 44.5% and 25.8%, respectively. Physical sciences/Energy science and technology/Energy storage Physical sciences/Mathematics and computing lithium-ion battery remaining useful life feature fusion CNN health factor Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Nov, 2024 Reviews received at journal 04 Nov, 2024 Reviews received at journal 26 Oct, 2024 Reviewers agreed at journal 14 Oct, 2024 Reviewers agreed at journal 12 Oct, 2024 Reviewers invited by journal 12 Oct, 2024 Editor assigned by journal 12 Oct, 2024 Editor invited by journal 30 Sep, 2024 Submission checks completed at journal 28 Sep, 2024 First submitted to journal 02 Sep, 2024 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-5015371","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375906945,"identity":"ea186e2a-bd3f-43e1-b5f6-a93cc95d2f52","order_by":0,"name":"Lin Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIie3PsUoEMRCA4QkDe80saW9B3FcYsRDh0FfJIljt+QoGFuIrZN/CSq6c5cBq0fbgqusF9zotRHNubfZKi/yQwMB8kACkUv8zJQbgNJs1AsouSGs7bQ7kXNOzCeT2pPByBAmnan3NgawXbE18vfRLkd0K1KMYxnz1Sgyihn39N+HNnZGqB2QRg3m/pQu0WLRPETKvWSoHGXdWcOm2dGklwzxCSj8S4rWygbwQi4kT2IxkXjiEQGSacP/2S1hTBvjlbqjwXRP9S/lQn+0+Hdy78n1Qrbu61rrphn3sYWPfh2v2MQ7KTu6nUqlUKt4P6+RUdE8PtHkAAAAASUVORK5CYII=","orcid":"","institution":"Basic Science Department，Wenhua College","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Sun","suffix":""},{"id":375906946,"identity":"329a0154-9da9-43ff-a660-3f8b99f9213f","order_by":1,"name":"Xiaojie Huang","email":"","orcid":"","institution":"National University of Defense Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaojie","middleName":"","lastName":"Huang","suffix":""},{"id":375906947,"identity":"972f836f-52cf-4246-8331-d3f0c6ed73cb","order_by":2,"name":"Jing Liu","email":"","orcid":"","institution":"National University of Defense Technology","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liu","suffix":""},{"id":375906948,"identity":"e24269a4-1330-4583-991d-fd0f02e6cec6","order_by":3,"name":"Jing Song","email":"","orcid":"","institution":"National University of Defense Technology","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Song","suffix":""},{"id":375906949,"identity":"3e2a17f2-b04b-4709-b68b-64a18ece39a6","order_by":4,"name":"Shimiao Wu","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shimiao","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-09-02 04:40:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5015371/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5015371/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-08619-6","type":"published","date":"2025-09-25T15:58:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92430611,"identity":"9436cc91-da73-4f33-818d-bac59197f547","added_by":"auto","created_at":"2025-09-29 16:06:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":875435,"visible":true,"origin":"","legend":"","description":"","filename":"newmanuscript03.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5015371/v1_covered_3c4dc535-ac69-4c2c-809b-2bbc6d34536d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Remaining useful life prediction of lithium batteries based on jump- connected multi-scale CNN","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"lithium-ion battery, remaining useful life, feature fusion, CNN, health factor","lastPublishedDoi":"10.21203/rs.3.rs-5015371/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5015371/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to better utilize the feature information obtained by all convolutional layers in Convolutional Neural Networks (CNN), this paper proposes a lithium-ion battery remaining life prediction model based on jump connected multi-scale CNN. The proposed model takes the health factor of the battery as input, and uses the multi-scale CNN model based on jump connection to extract the local feature information and global feature information of the health factor of lithium-ion battery at different scales. Then, all the local feature information and global feature information are fused through the information fusion module, and finally the predicted remaining useful life is output. The experimental results show that the proposed method can predict the remaining useful life of lithium-ion batteries more accurately. Compared with the classical CNN method, Bi-LSTM method, EMD-LSTM method and VMD-GRU method, the root mean square error (ERMSE) of the proposed method is reduced by 75.7%, 78.3%, 83.8% and 77.8%, respectively. Mean absolute error (EMAE) decreased by 80.7%, 80.9%, 86.8%, 82.3%, and mean absolute percentage error (EMAPE) decreased by 81.0%, 82.2%, 87.0%, 83.1%, respectively. The model determination coefficient (R2) increased by 17.4%, 23.2%, 44.5% and 25.8%, respectively.\u003c/p\u003e","manuscriptTitle":"Remaining useful life prediction of lithium batteries based on jump- connected multi-scale CNN","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-12 03:24:24","doi":"10.21203/rs.3.rs-5015371/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-08T19:51:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-05T03:48:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-26T18:54:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6388703250223980424381066055453164813","date":"2024-10-14T13:20:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17664619190858372000515260249510015874","date":"2024-10-12T11:43:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-12T10:53:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-12T10:50:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-30T13:11:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-28T09:53:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-09-02T04:38:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a4cddcd0-a61f-4969-bec7-c706739ba486","owner":[],"postedDate":"December 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40024456,"name":"Physical sciences/Energy science and technology/Energy storage"},{"id":40024457,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-09-29T16:02:32+00:00","versionOfRecord":{"articleIdentity":"rs-5015371","link":"https://doi.org/10.1038/s41598-025-08619-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-25 15:58:20","publishedOnDateReadable":"September 25th, 2025"},"versionCreatedAt":"2024-12-12 03:24:24","video":"","vorDoi":"10.1038/s41598-025-08619-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-08619-6","workflowStages":[]},"version":"v1","identity":"rs-5015371","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5015371","identity":"rs-5015371","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}