Benchmarking Adaptive EV Charging Recommendation Under Feasibility Constraints and Non-Stationary Infrastructure

Benchmarking Adaptive EV Charging Recommendation Under Feasibility Constraints and Non-Stationary Infrastructure | 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 Benchmarking Adaptive EV Charging Recommendation Under Feasibility Constraints and Non-Stationary Infrastructure vibhor joshi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9415800/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 The rapid adoption of electric vehicles (EVs) has intensified the need for charging station recommendation systems that can adapt continuously to changing infrastructure conditions. In practice, charging environments are highly dynamic: station availability fluctuates; prices vary over time, and charging capabilities differ widely across locations. However, many existing approaches assume static infrastructure attributes or optimize a single objective in isolation, limiting their effectiveness in real-world deployments where robust adaptation under uncertainty is essential. In this work, we study EV charging recommendations as a constraint-aware online decision problem and present a unified benchmarking framework for evaluating learning-based recom mendation policies under both static and dynamic regimes. Using a large-scale global charg ing infrastructure dataset (1.5M stations) and a real-world time-series availability and pricing dataset, we compare contextual bandit and reinforcement learning approaches across multiple dimensions, including relevance, feasibility, regret, adaptability, and recovery from exogenous shocks. Our results show that learning-based methods significantly reduce constraint violations and cumulative regret compared to heuristic baselines in static settings, while neural contextual bandits achieve the strongest feasibility–utility trade-off. Under dynamic conditions, we ob serve clear differences in adaptation speed and shock recovery across algorithms, indicating that effective EV charging recommendation requires not only feasibility-aware reward design, but also robustness to non-stationary pricing and availability and the preservation of a meaningful learning signal during disruptive events Artificial Intelligence and Machine Learning Electric vehicle charging recommendation systems benchmarking contextual bandits reinforcement learning feasibility constraints non-stationary environments dynamic pricing charging station availability adaptive decision-making explainable recommendations Full Text Additional Declarations The authors declare no competing interests. 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-9415800","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622990927,"identity":"e5c4f2c7-1dc0-4b66-bdfa-8717902dbe5d","order_by":0,"name":"vibhor joshi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3PsQrCMBCA4ZNAXaK3puhDRASpUPRVFEGXIoKLkwiCk+Da0QdxSAnoorh2dHKuW0EHE63Qpa2jYP4lR7mPJgAm0y9GqPU6ACiIiDOafGffkcBXhBYSeBM9KP46CuKHyv5y20EZ8RRId+LUu7gtRTE440wiq6OGfwVi++Oe9PTF2IXYa2DTLGIvaatGBRAeUp4QATX1lv6ikJyPXDpvQu55BMmHCI9L0AQXVu5fkFSHti+YeovHg7UmYWnVVkMmsfC0Z5FwB4jHZhQ/5t3yRsowns0zSRIbpGa9zPP3dZ3UXUXxuslkMv1VT+l7RbCJFkXfAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0003-1074-0774","institution":"indian Institute of information technology guwahati","correspondingAuthor":true,"prefix":"","firstName":"vibhor","middleName":"","lastName":"joshi","suffix":""}],"badges":[],"createdAt":"2026-04-14 13:03:45","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9415800/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9415800/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106994363,"identity":"db4263ee-81ae-4939-abe4-6001c14b4de5","added_by":"auto","created_at":"2026-04-15 15:08:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":779071,"visible":true,"origin":"","legend":"","description":"","filename":"updatedpaper2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9415800/v1_covered_58f482d3-4912-4c6e-88d3-6f76327e8075.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBenchmarking Adaptive EV Charging Recommendation Under Feasibility Constraints and Non-Stationary Infrastructure\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Electric vehicle charging, recommendation systems, benchmarking, contextual bandits, reinforcement learning, feasibility constraints, non-stationary environments, dynamic pricing, charging station availability, adaptive decision-making, explainable recommendations","lastPublishedDoi":"10.21203/rs.3.rs-9415800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9415800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid adoption of electric vehicles (EVs) has intensified the need for charging station recommendation systems that can adapt continuously to changing infrastructure conditions. In practice, charging environments are highly dynamic: station availability fluctuates; prices vary over time, and charging capabilities differ widely across locations. However, many existing approaches assume static infrastructure attributes or optimize a single objective in isolation, limiting their effectiveness in real-world deployments where robust adaptation under uncertainty is essential.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this work, we study EV charging recommendations as a constraint-aware online decision problem and present a unified benchmarking framework for evaluating learning-based recom mendation policies under both static and dynamic regimes. Using a large-scale global charg ing infrastructure dataset (1.5M stations) and a real-world time-series availability and pricing dataset, we compare contextual bandit and reinforcement learning approaches across multiple dimensions, including relevance, feasibility, regret, adaptability, and recovery from exogenous shocks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results show that learning-based methods significantly reduce constraint violations and cumulative regret compared to heuristic baselines in static settings, while neural contextual bandits achieve the strongest feasibility–utility trade-off. 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