A Temporal Graph Attention Framework for Predictive Congestion Management in Vehicle-to-Vehicle Energy Sharing Networks

A Temporal Graph Attention Framework for Predictive Congestion Management in Vehicle-to-Vehicle Energy Sharing Networks | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 March 2026 V1 Latest version Share on A Temporal Graph Attention Framework for Predictive Congestion Management in Vehicle-to-Vehicle Energy Sharing Networks Author : NOUR ABU JASSAR 0009-0001-9517-776X [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177282012.27887476/v1 155 views 59 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Accelerating electric vehicle (EV) deployment is constrained by uneven charging infrastructure, driver range anxiety, and persistent mismatches between where energy is demanded and where it is available. Vehicle-to-vehicle (V2V) energy sharing alleviates these pressures by treating every EV as a mobile storage asset, yet current coordination schemes react to congestion rather than anticipating it, and rarely optimize multiple service objectives simultaneously. This paper proposes a three-layer temporal graph architecture for intelligent V2V energy sharing. The first layer continuously constructs a bipartite graph over EVs and charging stations, encoding proximity, state of charge, queue occupancy, and dynamic pricing on each edge. The second layer applies a Graph Attention Network (GAT) trained on historical session data to predict congestion probabilities on candidate provider edges before service degradation occurs. The third layer resolves each energy request through a multi-criteria matching function that jointly minimizes response latency, maximizes energy efficiency, distributes provider load equitably, and controls service cost. Evaluated on 10,000 realworld DC fast-charging sessions from the DESL-EPFL dataset, which are transformed into realistic V2V scenarios through a principled simulation pipeline, the proposed framework achieves an 85.0% fulfillment rate with a mean response time of 29.6 minutes. These figures represent a 9.0% gain in service success and a 30.0% reduction in waiting time relative to the strongest greedy baseline. The GAT predictor reaches 87.4% overall accuracy and enables proactive resource allocation that cuts congestion driven failures by 51.7%. Scalability experiments confirm sub-250 ms processing for networks of up to 5,000 nodes, supporting city-scale real-time deployment. Supplementary Material File (v2v_energy_sharing_presentation (1).mp4) Download 9.00 MB File (vehicle_to_vehicle_energy_sharing_paper____.pdf) Download 1.14 MB Information & Authors Information Version history V1 Version 1 06 March 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords congestion prediction electric vehicles graph attention networks load balancing multi-objective optimization sustainable urban mobility vehicle-to-vehicle energy sharing Authors Affiliations NOUR ABU JASSAR 0009-0001-9517-776X [email protected] Department of Computer Engineering, The Hashemite University View all articles by this author Metrics & Citations Metrics Article Usage 155 views 59 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation NOUR ABU JASSAR. A Temporal Graph Attention Framework for Predictive Congestion Management in Vehicle-to-Vehicle Energy Sharing Networks. Authorea . 06 March 2026. DOI: https://doi.org/10.22541/au.177282012.27887476/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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