Artificial intelligence-based predictive reference model for lithium iron phosphate battery cell aging analysis

Artificial intelligence-based predictive reference model for lithium iron phosphate battery cell aging analysis | 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 Artificial intelligence-based predictive reference model for lithium iron phosphate battery cell aging analysis Raja Yahmadi, Kais Brik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8481916/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 This study introduces an Artificial intelligence (AI) approach to model the discharge voltage characteristics of a new Lithium-Iron Phosphate (LFP) battery cell under different operating conditions and to use it as a reference for healthy assessment. Experimental voltage-State Of Charge (SOC) data were obtained from a new cell at three temperatures (0°C,25°C, and 45°C) and for several discharge currents. In order to predict the appropriate discharge voltage behavior under any operating conditions, a Gaussian Process Regression (GPR) model was trained using temperature, discharge current, and SOC as input variables. The trained model provides a continuous voltage reference under any realistic combination of temperature and current. Based on this reference, a diagnostic system was developed to compare the measured discharge voltage of cycled cells with the reference voltage of a new cell under the same conditions. The deviation between the predicted and measured voltages enables the estimation of State of Health (SOH) and allows assessing whether a manufactured cell exhibits early degradation. This approach provides a fast and efficient solution for cell quality assessment and early detection of abnormal degradation. The results demonstrate that the proposed AI based reference model enables reliable SOH evaluation, offering strong potential for industrial diagnostic applications and manufacturing quality control. LFP cell artificial intelligence Gaussian Process Regression (GPR) sate of health prediction diagnostic system 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-8481916","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":568661375,"identity":"b7d2a757-e042-4cb1-8861-68f896813d73","order_by":0,"name":"Raja Yahmadi","email":"","orcid":"","institution":"University of Carthage","correspondingAuthor":false,"prefix":"","firstName":"Raja","middleName":"","lastName":"Yahmadi","suffix":""},{"id":568661378,"identity":"6a112788-6812-49ab-8727-8416390f66eb","order_by":1,"name":"Kais 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Experimental voltage-State Of Charge (SOC) data were obtained from a new cell at three temperatures (0\u0026deg;C,25\u0026deg;C, and 45\u0026deg;C) and for several discharge currents. In order to predict the appropriate discharge voltage behavior under any operating conditions, a Gaussian Process Regression (GPR) model was trained using temperature, discharge current, and SOC as input variables. The trained model provides a continuous voltage reference under any realistic combination of temperature and current. Based on this reference, a diagnostic system was developed to compare the measured discharge voltage of cycled cells with the reference voltage of a new cell under the same conditions. The deviation between the predicted and measured voltages enables the estimation of State of Health (SOH) and allows assessing whether a manufactured cell exhibits early degradation. This approach provides a fast and efficient solution for cell quality assessment and early detection of abnormal degradation. 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