Simultaneous Prediction of Three Key Li-Ion Battery Indicators through Multi-Task Learning

Simultaneous Prediction of Three Key Li-Ion Battery Indicators through Multi-Task Learning | 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 Simultaneous Prediction of Three Key Li-Ion Battery Indicators through Multi-Task Learning Minjeong Gong, Yoonjung Choi, Han Mo Yang, Sang Bok Ma, Dong-Hwa Seo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9212597/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 diagnosis of battery states is essential for ensuring operational safety, evaluating current performance, and assessing suitability for second-life applications. However, characterizing key indicators such as State of Health (SoH), Direct Current Internal Resistance (DCIR), and maximum cell temperature during operation (Max. temp.) remains highly time-intensive because it requires low-current measurements and separate testing procedures. The challenge is further compounded by the weak linearity and large variance in the relationships among these indicators. To address these challenges, we developed a multi-task learning (MTL) framework that combined a rapid diagnostic protocol with simultaneous prediction of the three key battery indicators. Using features extracted from the rapid diagnostic protocol, a neural network model was trained to predict multiple targets concurrently. The proposed MTL framework outperformed single-task baselines, reducing the root mean squared error by 25.9% for SoH, 25.0% for the DCIR increase rate, and 29.4% for Max. temp. These performance gains arise from the joint learning of highly correlated targets, which enables the model to capture coupled degradation behavior and promotes positive transfer and inductive bias. By integrating a rapid diagnostic protocol with an MTL, this study provides an accurate and data-efficient framework for simultaneous prediction of multiple degradation-relevant battery indicators. Full Text Additional Declarations The authors declare no competing interests. Supplementary Files MTLmanuscriptsupplementaryinformation.docx 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. 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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-9212597","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611408749,"identity":"a932f61a-3a93-4b36-b8bf-ada1261c08b2","order_by":0,"name":"Minjeong Gong","email":"","orcid":"","institution":"Korea Advanced Institute of Science and Technology (KAIST)","correspondingAuthor":false,"prefix":"","firstName":"Minjeong","middleName":"","lastName":"Gong","suffix":""},{"id":611408750,"identity":"45cca82a-8940-4ee6-8751-46034c20f738","order_by":1,"name":"Yoonjung Choi","email":"","orcid":"","institution":"Korea Advanced Institute of 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