An algorithm based on Artificial Protozoa Optimizer and generalized correntropy-improved adaptive genetic particle filter for the state of charge estimation of lithium-ion batteries

An algorithm based on Artificial Protozoa Optimizer and generalized correntropy-improved adaptive genetic particle filter for the state of charge estimation of lithium-ion batteries | 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 An algorithm based on Artificial Protozoa Optimizer and generalized correntropy-improved adaptive genetic particle filter for the state of charge estimation of lithium-ion batteries Jinyang Lin, Guizhao Li, Tihao Gao, Shuhua Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8053895/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Accurate estimation of the state of charge (SOC) in lithium-ion batteries is critical for the safe and efficient operation of battery management systems (BMS). In real-world conditions, BMS may work in complex and changeable working conditions, which leads to measurement data being affected by non-Gaussian noise (or outliers). This paper proposes an algorithm for SOC estimation that combines an Artificial Protozoa Optimizer (APO) and an improved adaptive genetic particle filter (IAGPF) based on generalized correntropy (GC) to address this challenge. By establishing a second-order fractional-order model (FOM) for lithium-ion battery and conducting simulation experiments. Experimental results show that the proposed algorithm outperforms conventional methods, such as the fractional-order particle filter (FOPF) and fractional-order improved genetic particle filter (FOIGPF), across various non-Gaussian environments and operational scenarios. The algorithm achieves a mean absolute error (MAE) and root mean square error (RMSE) both below 1%, demonstrating its robustness and high estimation accuracy. Artificial Protozoa Optimizer Generalized Correntropy Lithium-ion Batteries Particle Filter State Of Charge Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Jan, 2026 Reviews received at journal 03 Jan, 2026 Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 17 Nov, 2025 Submission checks completed at journal 17 Nov, 2025 First submitted to journal 07 Nov, 2025 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. 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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-8053895","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560648491,"identity":"8625aaa8-a891-4ab4-9390-da49a90ad656","order_by":0,"name":"Jinyang 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Optimizer and generalized correntropy-improved adaptive genetic particle filter for the state of charge estimation of lithium-ion batteries","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"ionics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":" Learn more about [Ionics](https://www.springer.com/journal/11581) ","snPcode":"11581","submissionUrl":"https://mc.manuscriptcentral.com/ionics","title":"Ionics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Artificial Protozoa Optimizer, Generalized Correntropy, Lithium-ion Batteries, Particle Filter, State Of Charge","lastPublishedDoi":"10.21203/rs.3.rs-8053895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8053895/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 critical for the safe and efficient operation of battery management systems (BMS). In real-world conditions, BMS may work in complex and changeable working conditions, which leads to measurement data being affected by non-Gaussian noise (or outliers). This paper proposes an algorithm for SOC estimation that combines an Artificial Protozoa Optimizer (APO) and an improved adaptive genetic particle filter (IAGPF) based on generalized correntropy (GC) to address this challenge. By establishing a second-order fractional-order model (FOM) for lithium-ion battery and conducting simulation experiments. Experimental results show that the proposed algorithm outperforms conventional methods, such as the fractional-order particle filter (FOPF) and fractional-order improved genetic particle filter (FOIGPF), across various non-Gaussian environments and operational scenarios. The algorithm achieves a mean absolute error (MAE) and root mean square error (RMSE) both below 1%, demonstrating its robustness and high estimation accuracy.\u003c/p\u003e","manuscriptTitle":"An algorithm based on Artificial Protozoa Optimizer and generalized correntropy-improved adaptive genetic particle filter for the state of charge estimation of lithium-ion batteries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 04:02:42","doi":"10.21203/rs.3.rs-8053895/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-22T21:13:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-03T13:22:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-15T14:43:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317362955371549101759881787598663001855","date":"2025-12-11T03:34:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227390678228272352457024503191294840276","date":"2025-12-10T20:52:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T18:48:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-17T09:12:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-17T09:12:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Ionics","date":"2025-11-07T06:49:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ionics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":" Learn more about [Ionics](https://www.springer.com/journal/11581) ","snPcode":"11581","submissionUrl":"https://mc.manuscriptcentral.com/ionics","title":"Ionics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"35c779a5-2867-43af-8c45-8a1b4e649ae6","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-15T06:53:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 04:02:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8053895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8053895","identity":"rs-8053895","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}