Predictive Graph Neural Network Framework for Congestion Aware V2V Energy Sharing

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.