Context-Aware Multi-Agent Coordination Framework for Intelligent Electric Vehicle Charging Optimization.

The accelerating transition toward electric mobility presents unprecedented challenges in coordinating distributed charging infrastructure across multi-stakeholder ecosystems. This paper introduces a novel Context-Aware Multi-Agent Coordination for Dynamic Resource Allocation (CAMAC-DRA) framework that addresses these challenges through intelligent integration of Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL). Our framework enables autonomous coordination of 250 electric vehicles (EVs) across 45 charging stations while dynamically adapting to real-time environmental conditions including weather patterns, traffic dynamics, grid fluctuations, and electricity pricing mechanisms. The proposed system processes 20 contextual features through sophisticated attention mechanisms, implementing hierarchical coordination protocols that balance competing objectives across five key stakeholder groups: EV users (25% weight), grid operators (20%), charging station operators (20%), fleet operators (20%), and environmental entities (15%). Through comprehensive validation using real-world datasets encompassing 441,077 charging transactions from diverse operational scenarios, the CAMAC-DRA framework demonstrates substantial improvements over state-of-the-art baseline algorithms including Deep Deterministic Policy Gradient (DDPG), Asynchronous Advantage Actor-Critic (A3C), Proximal Policy Optimization (PPO), and conventional GNN approaches. Experimental results reveal exceptional performance metrics: 92% multi-agent coordination success rate, 15% energy efficiency improvement, 10% operational cost reduction, 20% grid strain mitigation, and 2.3× faster convergence compared to existing methods, while maintaining 88% training stability and 85% sample efficiency. Real-world validation confirms commercial viability with a Net Present Cost of -122,962and6925.5 billion by 2029, and the broader transition toward carbon-neutral transportation infrastructure.