A Joint SOC Estimation Method Based on FFRLS-AEKF-LSTM

A Joint SOC Estimation Method Based on FFRLS-AEKF-LSTM | 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 A Joint SOC Estimation Method Based on FFRLS-AEKF-LSTM Min Wei, XianTao Yu, JiangCheng Ding This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6858550/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 The state of charge (SOC) of a battery is a core component of battery management systems (BMS) and has become a key research focus because of its significant role in the development of clean energy. To address the limitations of traditional SOC estimation methods, this paper proposes a joint SOC estimation algorithm based on FFRLS-AEKF-LSTM. First, a second-order RC equivalent circuit model of the battery is established, using data from hybrid pulse power characterization (HPPC) tests as input. The model parameters are initially identified the forgetting factor recursive least squares (FFRLS) method. Then, the adaptive extended Kalman filter (AEKF) algorithm is employed to update and estimate the SOC iteratively. Finally, the parameters obtained from the AEKF, together with voltage and current data under HPPC conditions, are used to train a long short-term memory (LSTM) neural network to predict the SOC. Experimental results show that the proposed joint algorithm achieves a root mean square error (RMSE) and mean absolute error (MAE) of less than 1%, demonstrating excellent performance. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Mechanical engineering Lithium-ion battery State of charge Long short-term memory network Full Text Additional Declarations No competing interests reported. Supplementary Files 6.16describe.docx 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-6858550","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486824626,"identity":"e7cb25be-1b94-4f8a-b855-58af2d93ef38","order_by":0,"name":"Min Wei","email":"","orcid":"","institution":"Wuhan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Wei","suffix":""},{"id":486824627,"identity":"99fecdcf-6917-4d59-89e8-f002abdbc543","order_by":1,"name":"XianTao Yu","email":"","orcid":"","institution":"Wuhan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"XianTao","middleName":"","lastName":"Yu","suffix":""},{"id":486824628,"identity":"455ab377-2011-4e14-a62e-6a5fe0079615","order_by":2,"name":"JiangCheng Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3RsQrCQAyA4ZTCuQS6pgh9A+FEUAuir3JF0KXubrYU2qUPoOBDCIKrVwrq5uqoCE4KujkIuurUcxO8f/9IQgB0uh/MssLker23RsskkmrEHuexMUl7xgxXQo1w2Y9NZLkxI58rbiazeI9omg3yb7sztJ1KUCCMKEw4NRlz08vcnUK3VpcFxIQsII6IsB4sygjSWxQRBl5AghGB9E9qBMGLSTLO+cZnaoQoi6phKoSdrmrulCvc0tkmh+Pj/hRWKTrszsO2U0g+R6Lqa97It0Kn0+n+oheS0EYFQ7bKHwAAAABJRU5ErkJggg==","orcid":"","institution":"Wuhan University of Technology","correspondingAuthor":true,"prefix":"","firstName":"JiangCheng","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2025-06-10 03:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6858550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6858550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108805421,"identity":"91d7b45a-c32e-4a44-9510-4594fd114006","added_by":"auto","created_at":"2026-05-08 15:25:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":606017,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedVersion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6858550/v1_covered_3d0aae56-ec31-448b-9682-973fc1433d0b.pdf"},{"id":87053525,"identity":"e85e40b2-f9c9-4945-b925-f43ea9b21bf2","added_by":"auto","created_at":"2025-07-18 15:13:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19082,"visible":true,"origin":"","legend":"","description":"","filename":"6.16describe.docx","url":"https://assets-eu.researchsquare.com/files/rs-6858550/v1/4d8eddfb124ef2bbc7b7faa9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Joint SOC Estimation Method Based on FFRLS-AEKF-LSTM","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 charge, Long short-term memory network","lastPublishedDoi":"10.21203/rs.3.rs-6858550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6858550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe state of charge (SOC) of a battery is a core component of battery management systems (BMS) and has become a key research focus because of its significant role in the development of clean energy. To address the limitations of traditional SOC estimation methods, this paper proposes a joint SOC estimation algorithm based on FFRLS-AEKF-LSTM. First, a second-order RC equivalent circuit model of the battery is established, using data from hybrid pulse power characterization (HPPC) tests as input. The model parameters are initially identified the forgetting factor recursive least squares (FFRLS) method. Then, the adaptive extended Kalman filter (AEKF) algorithm is employed to update and estimate the SOC iteratively. Finally, the parameters obtained from the AEKF, together with voltage and current data under HPPC conditions, are used to train a long short-term memory (LSTM) neural network to predict the SOC. Experimental results show that the proposed joint algorithm achieves a root mean square error (RMSE) and mean absolute error (MAE) of less than 1%, demonstrating excellent performance.\u003c/p\u003e","manuscriptTitle":"A Joint SOC Estimation Method Based on FFRLS-AEKF-LSTM","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 15:12:58","doi":"10.21203/rs.3.rs-6858550/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":"c793edb1-6ae1-4cc4-a0a0-ca38162ce1f4","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-06T09:00:03+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51684252,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":51684253,"name":"Physical sciences/Engineering/Mechanical engineering"}],"tags":[],"updatedAt":"2026-05-06T09:12:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 15:12:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6858550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6858550","identity":"rs-6858550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}