Scalable Reinforcement Learning for Large-Scale Coordination of Electric Vehicles Using Graph Neural Networks

Scalable Reinforcement Learning for Large-Scale Coordination of Electric Vehicles Using Graph Neural Networks | 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 Scalable Reinforcement Learning for Large-Scale Coordination of Electric Vehicles Using Graph Neural Networks Stavros Orfanoudakis, Valentin Robu, Edgar Mauricio Salazar Duque, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5504138/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Communications Engineering → Version 1 posted You are reading this latest preprint version Abstract As the adoption of electric vehicles (EVs) accelerates, addressing the challenges of large-scale, city-wide optimization becomes critical to ensuring efficient use of charging infrastructure and maintaining electrical grid stability. This study introduces EV-GNN, a novel graph-based solution that addresses scalability challenges and captures uncertainties in EV behavior from a Charging Point Operator's (CPO) perspective. We prove that EV-GNN enhances classic RL algorithms' scalability and sample efficiency by combining an end-to-end Graph Neural Network (GNN) architecture with Reinforcement Learning (RL) and employing a branch pruning technique. We further demonstrate that the proposed architecture's flexibility allows it to be combined with most state-of-the-art deep RL algorithms to solve a wide range of problems, including those with continuous, multi-discrete, and discrete action spaces. Extensive experimental evaluations show that EV-GNN significantly outperforms state-of-the-art RL algorithms in scalability and generalization across diverse EV charging scenarios, delivering notable improvements in both small- and large-scale problems. Smart Charging Deep Learning Graph Neural Networks Reinforcement Learning Sequential Decision Making Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMaterialScalableReinforcementLearningforLargeScaleCoordinationofElectricVehiclesUsingGraphNeuralNetworks.pdf Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Communications Engineering → 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. 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