State of Charge Estimation of Power Batteries Based on a Multi-Model Fusion Method

State of Charge Estimation of Power Batteries Based on a Multi-Model Fusion Method | 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 State of Charge Estimation of Power Batteries Based on a Multi-Model Fusion Method jiabo Li, yufei Luo, di Tian, zhonglin Sun, zhixuan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8589846/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 Power batteries have significant time-varying nonlinearity, resulting in significant differences in the estimated state of charge (SOC) obtained from different equivalent circuit models. A fusion method for the SOC estimation of power batteries based on multiple model probabilities is proposed in this paper. Firstly, select three battery models, namely Thevenin model, DP model, and fractional order model, and the forgetting factor recursive least squares method(FFRLS) is used to identify the model parameters. Secondly, considering that traditional FFRLS parameter identification methods rely on fixed forgetting factors in the algorithm, the algorithm is optimized into a variable forgetting factor recursive least squares (VFFRLS) and is used for online identification of model parameters. Thirdly, the adaptive unscented Kalman filtering(AUKF) technique is applied to estimate the SOC of each of the three models shows that SOC estimation outcomes based on a sole model cannot always guarantee reliability. Additionally, to address the issues of low utilization of historical data when using the UKF for SOC estimation, as well as the potential failure of the algorithm in simulations due to non positive definite covariance matrices, a novel SOC framework is proposed. This framework combines the Multiple Innovations (MI) theory, Singular Value Decomposition (SVD), and Adaptive Unscented Kalman Filter (AUKF) algorithm, namely SVD-AMIUKF, and employs a Bayesian-based probabilistic method to integrate the SOC estimates obtained from these three models with optimal weighting, achieving accurate SOC estimation. Finally, the proposed model is validated under different experimental conditions. The experimental results show that under DST working conditions, the average absolute percentage error (MAPE) of the proposed model is 0.00753. Compared with the estimation results of the Thevenin model, DP model, and fractional order model, the estimation accuracy has improved by 69.8%, 12.9%, and 55%, respectively. Under FUDS working conditions, the proposed model estimates a MAPE of 0.0028 for SOC. Compared with the above three models, the estimation accuracy has been improved by 70.5%, 69.8%, and 74.5% respectively, which verifies the effectiveness of the proposed method. power battery state of charge estimation multi-model fusion least square method unscented Kalman filter 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-8589846","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576879893,"identity":"c0412caa-8447-4f77-be0f-5ce9f40d4bae","order_by":0,"name":"jiabo 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Method","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":"power battery, state of charge estimation, multi-model fusion, least square method, unscented Kalman filter","lastPublishedDoi":"10.21203/rs.3.rs-8589846/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8589846/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePower batteries have significant time-varying nonlinearity, resulting in significant differences in the estimated state of charge (SOC) obtained from different equivalent circuit models. A fusion method for the SOC estimation of power batteries based on multiple model probabilities is proposed in this paper. Firstly, select three battery models, namely Thevenin model, DP model, and fractional order model, and the forgetting factor recursive least squares method(FFRLS) is used to identify the model parameters. Secondly, considering that traditional FFRLS parameter identification methods rely on fixed forgetting factors in the algorithm, the algorithm is optimized into a variable forgetting factor recursive least squares (VFFRLS) and is used for online identification of model parameters. Thirdly, the adaptive unscented Kalman filtering(AUKF) technique is applied to estimate the SOC of each of the three models shows that SOC estimation outcomes based on a sole model cannot always guarantee reliability. Additionally, to address the issues of low utilization of historical data when using the UKF for SOC estimation, as well as the potential failure of the algorithm in simulations due to non positive definite covariance matrices, a novel SOC framework is proposed. This framework combines the Multiple Innovations (MI) theory, Singular Value Decomposition (SVD), and Adaptive Unscented Kalman Filter (AUKF) algorithm, namely SVD-AMIUKF, and employs a Bayesian-based probabilistic method to integrate the SOC estimates obtained from these three models with optimal weighting, achieving accurate SOC estimation. Finally, the proposed model is validated under different experimental conditions. The experimental results show that under DST working conditions, the average absolute percentage error (MAPE) of the proposed model is 0.00753. Compared with the estimation results of the Thevenin model, DP model, and fractional order model, the estimation accuracy has improved by 69.8%, 12.9%, and 55%, respectively. Under FUDS working conditions, the proposed model estimates a MAPE of 0.0028 for SOC. Compared with the above three models, the estimation accuracy has been improved by 70.5%, 69.8%, and 74.5% respectively, which verifies the effectiveness of the proposed method.\u003c/p\u003e","manuscriptTitle":"State of Charge Estimation of Power Batteries Based on a Multi-Model Fusion Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 08:00:44","doi":"10.21203/rs.3.rs-8589846/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":"134a810e-9870-4291-b715-b943ba8c656e","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T14:40:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 08:00:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8589846","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8589846","identity":"rs-8589846","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}