DOVA-PATBM: An Intelligent, Adaptive, and Scalable Framework for Optimizing Large-Scale EV Charging Infrastructure

2025-06-18

AI Summary

To address the challenge of jointly optimizing coverage, equity, and grid constraints in electric vehicle (EV) charging infrastructure planning across urban–rural regions, this paper proposes a multi-source geospatial data-driven co-location framework for DC fast-charging networks. The method innovatively integrates H3 spatial indexing, Voronoi-constrained graph neural networks (GNNs) for regional importance assessment, POI-aware M/M/c temporal queuing modeling, and revenue-weighted greedy coverage optimization—enabling unified, multi-scale, grid-friendly, and equity-aware deployment. Empirical evaluation in Georgia demonstrates a 12-percentage-point increase in population coverage within 30 km, a 50% reduction in average access distance for low-income communities, and full compliance with distribution network capacity limits and outage risk constraints. The framework provides a scalable, reusable technical foundation for state-level and larger-scale EV charging infrastructure planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The accelerating uptake of battery-electric vehicles demands infrastructure planning tools that are both data-rich and geographically scalable. Whereas most prior studies optimise charging locations for single cities, state-wide and national networks must reconcile the conflicting requirements of dense metropolitan cores, car-dependent exurbs, and power-constrained rural corridors. We present DOVA-PATBM (Deployment Optimisation with Voronoi-oriented, Adaptive, POI-Aware Temporal Behaviour Model), a geo-computational framework that unifies these contexts in a single pipeline. The method rasterises heterogeneous data (roads, population, night lights, POIs, and feeder lines) onto a hierarchical H3 grid, infers intersection importance with a zone-normalised graph neural network centrality model, and overlays a Voronoi tessellation that guarantees at least one five-port DC fast charger within every 30 km radius. Hourly arrival profiles, learned from loop-detector and floating-car traces, feed a finite M/M/c queue to size ports under feeder-capacity and outage-risk constraints. A greedy maximal-coverage heuristic with income-weighted penalties then selects the minimum number of sites that satisfy coverage and equity targets. Applied to the State of Georgia, USA, DOVA-PATBM (i) increases 30 km tile coverage by 12 percentage points, (ii) halves the mean distance that low-income residents travel to the nearest charger, and (iii) meets sub-transmission headroom everywhere -- all while remaining computationally tractable for national-scale roll-outs. These results demonstrate that a tightly integrated, GNN-driven, multi-resolution approach can bridge the gap between academic optimisation and deployable infrastructure policy.

Problem

• Optimizes large-scale EV charging infrastructure for diverse geographic needs
• Balances urban, suburban, and rural charging demands efficiently
• Ensures equitable charger access while maintaining power grid stability

Innovation

• Uses H3 grid for hierarchical data rasterization
• Applies GNN centrality for intersection importance
• Employs Voronoi tessellation for charger coverage