Predictive Graph Neural Network Framework for Congestion Aware V2V Energy Sharing

Predictive Graph Neural Network Framework for Congestion Aware V2V Energy Sharing | 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 Predictive Graph Neural Network Framework for Congestion Aware V2V Energy Sharing NOUR ABU JASSAR This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8767957/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 widespread adoption of electric vehicles (EVs) is constrained by charg- ing infrastructure limitations, range anxiety, and temporal-spatial mismatches between energy demand and supply. Vehicle-to-vehicle (V2V) energy sharing presents a promising solution by leveraging EVs as distributed energy storage units, yet existing approaches lack predictive capabilities for managing net- work congestion and optimizing resource allocation at scale. This paper intro- duces a novel three-layer graph-based architecture for intelligent V2V energy sharing that integrates dynamic graph construction, Graph Attention Network (GAT)-based congestion prediction, and multi-objective routing optimization. The system models EVs and charging stations as nodes in a temporal bipartite graph, employs deep learning to forecast network bottlenecks, and implements an enhanced matching algorithm that simultaneously optimizes response time, energy efficiency, load distribution, and cost-effectiveness. Evaluated on 10,000 real-world DC fast-charging sessions transformed into V2V scenarios, the pro- posed framework achieves an 85.0% fulfillment rate with an average response time of 29.6 minutes, representing a 9.0% improvement in service success and a 30.0% reduction in wait times compared to greedy baselines. The GAT predictor demonstrates 87.4% accuracy in anticipating congestion, enabling proactive re- source allocation that reduces congestion-induced failures by 51.7%. This work extends existing urgent charge-sharing models by introducing predictive graph learning for large-scale V2V networks, offering a scalable solution for sustainable urban mobility infrastructure. Artificial Intelligence and Machine Learning Electrical Engineering Decision Sciences Systems and Networking Graphical Systems Information Retrieval and Management Software Engineering Graph Neural Networks Vehicle-to-Vehicle Energy Sharing Electric Vehicles Load Balancing Predictive Analytics Congestion Management Sustainable Transportation Full Text Additional Declarations The authors declare no competing interests. Supplementary Files NOURABUJASSARV2VEnergySharing1.pdf The Power Point Presentation NOURJAMALABUJASSARV2VEnergySharing1.mp4 The Explainer Video VehicletoVehicleEnergySharingPaper21.pdf The Paper 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. 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