AI-Enhanced Network Optimization for Electric Vehicle Charging Infrastructure Expansion in the United States Using Graph Theory and Demand Analytics

Abstract

With the growth of electric vehicles (EVs) in the United States, intelligent and scalable charging infrastructure optimization is required. The study presents an integrated network optimization framework that utilizes AI technology to overcome key drawbacks of the current network planning methods that rely on graph theory and demand analytics. The framework uses a Dynamic Spatiotemporal Graph Neural Network (D-ST-GNN) in its demand forecasting process and incorporates Bayesian optimization in its multi-objective charging station placement process. Results show that 78.3% of costs is reduced compared to traditional strategies, as the population is covered within 5 km with 96.8% efficiency, network efficiency is improved by 26.7%, queue times are reduced during peak hours by 32.1%, and there is equitable distribution with high accessibility index equal to 0.92. The D-ST-GNN model outperforms the accuracy targets with MAPE of 13.2% and R² of 0.87. Robustness of framework is confirmed with sensitivity analysis for ±20% demand change. The holistic solution enables policymakers to have a data-supported tool to decide on EV infrastructure deployment while keeping the cost-effectiveness, coverage, user experience, and equity in mind.

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