Joint Estimation of SOC and SOH for Lithium-ion Batteries Considering Various Temperatures and Life Cycles

Joint Estimation of SOC and SOH for Lithium-ion Batteries Considering Various Temperatures and Life Cycles | 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 Joint Estimation of SOC and SOH for Lithium-ion Batteries Considering Various Temperatures and Life Cycles Xifeng Guo, Yuhai Huang, Yi Ning, Di Zheng, Yinlei Wen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4666601/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 In addressing the challenge of joint estimation of State of Health (SOH) and State of Charge (SOC) for lithium-ion batteries under varying temperatures and aging conditions,this study proposes a method that analyzes the correlation between SOC and SOH and considers their mutual influence. The method begins by extracting health factors (HF) derived from voltage and current dynamics across different temperatures and SOC levels. Subsequently, a hybrid approach combining Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention Mechanisms (A) is employed (CNN-GRU-A) to estimate SOH. Utilizing battery's current, voltage and estimated SOH are used as inputs to predict SOC, taking into consideration factors contributing to degradation of Lithium-ion battery life. Experiments conducted on NASA datasets under various temperature conditions demonstrate that the proposed method achieves high-accuracy joint estimation of SOC and SOH across diverse operating scenarios. lithium-ion battery state of health state of charge improved global zebra optimization algorithm stochastic configuration network 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-4666601","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325431068,"identity":"220e142d-f811-40be-9399-15b0b75a7ca8","order_by":0,"name":"Xifeng Guo","email":"","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Xifeng","middleName":"","lastName":"Guo","suffix":""},{"id":325431069,"identity":"c061746b-880a-4edb-9cbf-51da7590b8bc","order_by":1,"name":"Yuhai Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3PsUrEQBCA4V0WxmZC2gSFe4U9LI6DQ19lDyE2V1imcySwlVivjY9hPWHhbBbTClcd9wKb7io1vZpoZ7FfPT8zI0SS/ENaSPLH+uPWzg4vx1ivMM9pMrnzIij5ICpTulCdlY6nEqG8tEo+iY0+zaxfaTLjyeLEk78JoEBszdxBh1qwjP3m52R5vybvagSQDZuIO1woUuXj88hhvCbGUOCwx7Su2OGSGFQ2lnR74szqAgDnDepX1GwmkrfhsMwaDYjnCg3/JtlT68IwVkAlHV9h6dpm/Jfu+hBjzWbm1Fb07xeXed60sR9JviPpb/NJkiTJF59x5Vrggl5tWAAAAABJRU5ErkJggg==","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":true,"prefix":"","firstName":"Yuhai","middleName":"","lastName":"Huang","suffix":""},{"id":325431070,"identity":"7ac18fe4-6d5c-480b-84b7-eccbb3608989","order_by":2,"name":"Yi Ning","email":"","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Ning","suffix":""},{"id":325431071,"identity":"d01632ee-f99b-43a3-9d94-2f16fe40a7c0","order_by":3,"name":"Di Zheng","email":"","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Zheng","suffix":""},{"id":325431072,"identity":"8b2c21c8-df49-433a-a375-96bc7d0149d6","order_by":4,"name":"Yinlei Wen","email":"","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Yinlei","middleName":"","lastName":"Wen","suffix":""}],"badges":[],"createdAt":"2024-07-01 08:51:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4666601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4666601/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66430183,"identity":"0ecc1ca3-2f2f-4527-a6b1-f9af3c2ceedf","added_by":"auto","created_at":"2024-10-11 19:46:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":870682,"visible":true,"origin":"","legend":"","description":"","filename":"JointEstimationofSOCandSOHforLithiumionBatteriesConsideringVariousTemperaturesandLifeCycles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4666601/v1_covered_bbb85143-884c-4019-a031-f7c8d4ae6f25.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint Estimation of SOC and SOH for Lithium-ion Batteries Considering Various Temperatures and Life Cycles","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, state of health, state of charge, improved global zebra optimization algorithm, stochastic configuration network","lastPublishedDoi":"10.21203/rs.3.rs-4666601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4666601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn addressing the challenge of joint estimation of State of Health (SOH) and State of Charge (SOC) for lithium-ion batteries under varying temperatures and aging conditions,this study proposes a method that analyzes the correlation between SOC and SOH and considers their mutual influence. The method begins by extracting health factors (HF) derived from voltage and current dynamics across different temperatures and SOC levels. Subsequently, a hybrid approach combining Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention Mechanisms (A) is employed (CNN-GRU-A) to estimate SOH. Utilizing battery's current, voltage and estimated SOH are used as inputs to predict SOC, taking into consideration factors contributing to degradation of Lithium-ion battery life. 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