Adaptive Extended Kalman Filter with Fuzzy Control Forgetting Factor for Lithium-ion Battery State-of-Charge Estimation

Adaptive Extended Kalman Filter with Fuzzy Control Forgetting Factor for Lithium-ion Battery State-of-Charge Estimation | 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 Adaptive Extended Kalman Filter with Fuzzy Control Forgetting Factor for Lithium-ion Battery State-of-Charge Estimation Shudong Guo, Shaojuan Fan, Fei Ye, Linli Yan, Yuan Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9386375/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 Accurate estimation of the state of charge (SOC) in lithium-ion batteries is crucial for battery management systems. Addressing the limitations of traditional fixed-forgetting-factor recursive least squares (FFRLS) and the insufficient robustness of extended Kalman filters (EKF) under noisy conditions, prior research has combined fuzzy control forgetting factors with adaptive extended Kalman filtering (AEKF) for SOC estimation in lithium cobalt oxide batteries. However, the applicability and effectiveness of this approach in mainstream power batteries remain under-validated. To address this, this paper takes typical power lithium-ion batteries as the research subject, systematically applying this method for the first time to two representative power lithium-ion battery types—ternary batteries (INR 18650-20R) and lithium iron phosphate batteries (A123)—to test its universal performance. By establishing a Thevenin model, parameter identification is performed using fuzzy adaptive FFRLS, combined with noise-adaptive AEKF to achieve SOC estimation. Experiments under dynamic stress testing (DST), Federal Urban Driving Schedule (FUDS), and varying temperature conditions demonstrate that, compared to conventional methods employing Forgetting Factor Recursive Least Squares (FFRLS) parameter identification coupled with traditional EKF or Adaptive Extended Kalman Filter (AEKF), this approach demonstrates superior estimation accuracy and enhanced robustness in both types of power batteries. This confirms its broad applicability and provides experimental justification for its implementation in power battery management systems. lithium-ion battery state estimation adaptive extended Kalman filter fuzzy control forgetfulness factor recursive least squares 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-9386375","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633469559,"identity":"b76e898a-2891-437c-a15a-e31d0f5e3c76","order_by":0,"name":"Shudong Guo","email":"","orcid":"","institution":"Anhui Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Shudong","middleName":"","lastName":"Guo","suffix":""},{"id":633469560,"identity":"560d4908-612c-4b04-ae66-b884bdfb2432","order_by":1,"name":"Shaojuan Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie2RMUvEMBTHUwKvS9pbewS/w5NCQDzazyKBTqEoLjfcUBCy6ezgB3GMFHrLHbdWXHSPgxwcOChGrTi11U0wPwgPwv/H/8EjxOP5g4QVIZR8vuA+qODjNxlSmPlWKP5KeQeSoCI/UMJ1vT1ezEJcrpv583Wco6E3d4xkZa/CyoJfNgXFVVncnq+AogF5yIg87VNyogRlULukEm2kAdAwwRkxR1Vfy8SmW/bqlI0VJy8aGJrJblhJFPJIO6V1da4lcS0woljBo4uCTlub8j0NOK0hPbhCObCYcovtZjLeqP2nR93k8fLsobXzrFf5Qnaz6c6EI3lH1s3FeNTj8Xj+HW8XZ04xrd+/DAAAAABJRU5ErkJggg==","orcid":"","institution":"Anhui Jianzhu University","correspondingAuthor":true,"prefix":"","firstName":"Shaojuan","middleName":"","lastName":"Fan","suffix":""},{"id":633469561,"identity":"63a1b377-ab74-4136-8390-adb95f665f31","order_by":2,"name":"Fei Ye","email":"","orcid":"","institution":"Anhui Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Ye","suffix":""},{"id":633469562,"identity":"3ebb7c6a-aa73-4322-8a38-084cca6b2df9","order_by":3,"name":"Linli Yan","email":"","orcid":"","institution":"Anhui Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Linli","middleName":"","lastName":"Yan","suffix":""},{"id":633469563,"identity":"284b1865-50cf-4c78-a41a-fa30ba0d156e","order_by":4,"name":"Yuan Liu","email":"","orcid":"","institution":"Anhui Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-04-11 09:09:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9386375/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9386375/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109378095,"identity":"2d2c39d0-faa6-4031-ae23-fd2b89e35f0f","added_by":"auto","created_at":"2026-05-16 15:40:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":842723,"visible":true,"origin":"","legend":"","description":"","filename":"11en12.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9386375/v1_covered_101f588a-1df1-4d44-9dd4-d2f0b105b9bf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Extended Kalman Filter with Fuzzy Control Forgetting Factor for Lithium-ion Battery State-of-Charge Estimation","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 estimation, adaptive extended Kalman filter, fuzzy control, forgetfulness factor recursive least squares","lastPublishedDoi":"10.21203/rs.3.rs-9386375/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9386375/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate estimation of the state of charge (SOC) in lithium-ion batteries is crucial for battery management systems. Addressing the limitations of traditional fixed-forgetting-factor recursive least squares (FFRLS) and the insufficient robustness of extended Kalman filters (EKF) under noisy conditions, prior research has combined fuzzy control forgetting factors with adaptive extended Kalman filtering (AEKF) for SOC estimation in lithium cobalt oxide batteries. However, the applicability and effectiveness of this approach in mainstream power batteries remain under-validated. To address this, this paper takes typical power lithium-ion batteries as the research subject, systematically applying this method for the first time to two representative power lithium-ion battery types\u0026mdash;ternary batteries (INR 18650-20R) and lithium iron phosphate batteries (A123)\u0026mdash;to test its universal performance. By establishing a Thevenin model, parameter identification is performed using fuzzy adaptive FFRLS, combined with noise-adaptive AEKF to achieve SOC estimation. Experiments under dynamic stress testing (DST), Federal Urban Driving Schedule (FUDS), and varying temperature conditions demonstrate that, compared to conventional methods employing Forgetting Factor Recursive Least Squares (FFRLS) parameter identification coupled with traditional EKF or Adaptive Extended Kalman Filter (AEKF), this approach demonstrates superior estimation accuracy and enhanced robustness in both types of power batteries. 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