Early Prediction of the Failure Probability Distribution for Energy Storage Technologies Driven by Domain-Knowledge-Informed Machine Learning

Early Prediction of the Failure Probability Distribution for Energy Storage Technologies Driven by Domain-Knowledge-Informed Machine 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 Article Early Prediction of the Failure Probability Distribution for Energy Storage Technologies Driven by Domain-Knowledge-Informed Machine Learning Maher Alghalayini, Stephen J. Harris, Marcus Noack This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3871499/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract There is a growing focus on sustainable energy sources and storage systems. The challenge with such emerging systems is their need to be warrantied for around 15 years with just a year of early testing. This requires accurate data extrapolation and estimation of the failure distribution. Physics-based approaches can be overwhelmed by the complexity of degradation, and pure data-driven approaches are inherently unable to extrapolate beyond the testing data. Here, we propose a framework for a hybrid approach for technology-agnostic customization of a Gaussian process for stochastic and domain-knowledge-informed failure distribution prediction. We equip the Gaussian process with customized non-stationary kernels, heteroscedastic noise models, and prior-mean functions to enable it to accurately extrapolate with high accuracy. Furthermore, we minimize testing time with a novel experiment-stopping criterion, which can significantly reduce the required data. Our framework could revolutionize energy-storage testing, enabling the rapid development of new technologies. Physical sciences/Energy science and technology/Energy storage/Batteries Physical sciences/Mathematics and computing/Statistics Physical sciences/Mathematics and computing/Applied mathematics Machine Learning Energy Storage Early Lifetime Prediction Failure Probability Distribution Gaussian Processes Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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-3871499","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267592169,"identity":"fa9b1ffc-6fe1-4bf2-85d4-e92a79a8f429","order_by":0,"name":"Maher Alghalayini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYFAC5gZmBgYLIM3AcOBDwQEZIrQwgrRIgLUcnGFwgIcELSALeYjRott+sPl1QY2EvMFx3oOHbQzu8DDwLz4mgU+L2ZnENusZxyQMNxzmSzicY/CMh0HiWRp+LQcS24x52CQYNxzmMQBqOQzUcsbYAK+W8w+BWv5J2IO1WBCl5UZi82PeNolEsBYGkBb+HsMH+LU8bGPm7ZNIngnUcrAH6Bc2CbZE/FrOJx/+zPPNxrbv/BnjDz8q7sjx8x8+cACfFiBgA4ePwgE4N4GABmAEfgCR8g0wPj8hO0bBKBgFo2CkAQCwOk4Y06LpMAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4071-0272","institution":"Lawrence Berkeley National Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Maher","middleName":"","lastName":"Alghalayini","suffix":""},{"id":267592170,"identity":"1452f85b-50f1-4288-b9db-7436a73f82fb","order_by":1,"name":"Stephen J. 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