An Advance and Hybrid State-of-Charge Measurement Technique for Battery Management System

An Advance and Hybrid State-of-Charge Measurement Technique for Battery Management System | 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 An Advance and Hybrid State-of-Charge Measurement Technique for Battery Management System Sarbajit Sarkar, Biplab Satpati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7973642/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 Battery Management System is a vital component in any battery driven device or facility. It is used to measure and control all the major battery parameters for better efficiency and performances. In today’s world each and every powered system is focused to get powered from electric supply. So, electric driven systems are the trending topic of research. In this paper, a proper discussion and analysis have been done on how the proposed solution is providing the State of Charge (SOC) measurement. The technique consists of a Non-linear Regression Model that is capable of analysing the dynamic behavior of the system and predict the SOC over time. The Coulomb Counting Method Model is to provide the initial value of SOC. Without initial value, the system cannot predict the current status of the system and cannot predict the SOC. Though the coulomb counting is used widely but there are some errors of measurement due to noise and other unnecessary signals. So, to remove those noises the measured values are passed through a modified Kalman filter to filter the prediction, leads to a better result that meets the dynamic behavior of the system accurately. This discussion also consists the overall testing and analysis of the system with a real time sample data to show it real time performance. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics State-of-Charge Non-linear Regression Least Square Method Coulomb counting Kalman Filter Hybrid method 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-7973642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":536735407,"identity":"8af6f5ac-5094-41eb-8157-55bc2bfa1fc4","order_by":0,"name":"Sarbajit 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It is used to measure and control all the major battery parameters for better efficiency and performances. In today\u0026rsquo;s world each and every powered system is focused to get powered from electric supply. So, electric driven systems are the trending topic of research. In this paper, a proper discussion and analysis have been done on how the proposed solution is providing the State of Charge (SOC) measurement. The technique consists of a Non-linear Regression Model that is capable of analysing the dynamic behavior of the system and predict the SOC over time. The Coulomb Counting Method Model is to provide the initial value of SOC. Without initial value, the system cannot predict the current status of the system and cannot predict the SOC. Though the coulomb counting is used widely but there are some errors of measurement due to noise and other unnecessary signals. So, to remove those noises the measured values are passed through a modified Kalman filter to filter the prediction, leads to a better result that meets the dynamic behavior of the system accurately. This discussion also consists the overall testing and analysis of the system with a real time sample data to show it real time performance.\u003c/p\u003e","manuscriptTitle":"An Advance and Hybrid State-of-Charge Measurement Technique for Battery Management System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 06:11:37","doi":"10.21203/rs.3.rs-7973642/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":"d6272972-0162-4641-8f85-0622bee7a896","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57073144,"name":"Physical sciences/Energy science and technology"},{"id":57073145,"name":"Physical sciences/Engineering"},{"id":57073146,"name":"Physical sciences/Mathematics and computing"},{"id":57073147,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2026-01-14T13:25:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 06:11:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7973642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7973642","identity":"rs-7973642","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}