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

• URL: http://arxiv.org/abs/2506.15289v1
• Date: Wed, 18 Jun 2025 09:15:18 GMT
• Title: DOVA-PATBM: An Intelligent, Adaptive, and Scalable Framework for Optimizing Large-Scale EV Charging Infrastructure
• Authors: Chuan Li, Shunyu Zhao, Vincent Gauthier, Hassine Moungla,
• Abstract summary: We present DOVA-PATBM (Deployment optimisation with Voronoi-oriented, Adaptive, POI-Aware Temporal Behaviour Model), a geo-computational framework that unifies contexts in a single pipeline.<n>The methodises heterogeneous data ( centrality, population, night lights, POIs, and feeder lines) onto a hierarchical H3 grid.<n>It infers intersection importance with a zone-normalised graph neural network model, and overlays a Voronoi tessellation that guarantees at least one five-port DC fast charger within every 30 km radius.
• Score: 3.74242093516574
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Multi-Dimensional AGV Path Planning in 3D Warehouses Using Ant Colony Optimization and Advanced Neural Networks [4.517879416915767]
  This paper introduces a novel AGV path planning approach for 3D warehouse environments that leverages a hybrid framework combining ACO and deep learning models.<n>NAHACO significantly boosts path planning efficiency, yielding faster computation times and superior performance over both vanilla and stateof-the-art methods.<n>In warehouse tests, NAHACO cuts cost by up to 41.5% and congestion by up to 56.1% compared to previous methods.
  arXiv  Detail & Related papers  (2025-03-30T14:09:21Z)
• Data-Driven Optimization of EV Charging Station Placement Using Causal Discovery [6.061650526072546]
  We analyze charging data from Palo Alto and Boulder to uncover latent relationships between station characteristics and utilization.<n>Applying structural learning algorithms to this data reveals that charging demand is primarily determined by three factors: proximity to amenities, EV registration density, and adjacency to high-traffic routes.<n>We develop an optimization framework that translates these insights into actionable placement recommendations.
  arXiv  Detail & Related papers  (2025-03-21T11:15:02Z)
• Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction [0.15978270011184256]
  We explore and adapt semi-decentralized training techniques for Spatiotemporal Graph-Temporal Neural Networks (ST-GNNs) in smart mobility domain.<n>We implement a simulation framework where sensors are grouped by proximity into multiple cloudlets.<n>We show that semi-decentralized setups are comparable to centralized approaches in performance metrics.
  arXiv  Detail & Related papers  (2024-12-04T10:20:21Z)
• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities [5.78463306498655]
  Electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and jeopardized stability. Modern power grids require coordinated or smart'' charging strategies capable of optimizing EV charging scheduling in a scalable and efficient fashion. We formulate a time-coupled binary optimization problem that maximizes EV users' total welfare gain while accounting for the network's available power capacity and stations' occupancy limits.
  arXiv  Detail & Related papers  (2023-05-18T14:03:47Z)
• Adaptive Federated Pruning in Hierarchical Wireless Networks [69.6417645730093]
  Federated Learning (FL) is a privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. In this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50 percent communication cost.
  arXiv  Detail & Related papers  (2023-05-15T22:04:49Z)
• Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach [79.59560136273917]
  limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL) We first devise a novel FL framework with partial model aggregation (PMA) The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-04-20T19:09:52Z)
• SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New City [19.95343057352923]
  It is difficult to predict charging demands before the actual deployment of EV chargers for lack of operational data. We propose Simultaneous Demand Prediction And Planning (SPAP) to solve this problem. SPAP improves at most 72.5% revenue compared with the real-world charger deployment.
  arXiv  Detail & Related papers  (2021-10-18T16:42:42Z)
• Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks [151.27147513363502]
  This paper studies the problem of the trajectory design for a group of energyconstrained drones operating in dynamic wireless network environments. A value based reinforcement learning (VDRL) solution and a metatraining mechanism is proposed.
  arXiv  Detail & Related papers  (2020-12-06T01:30:12Z)
• Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication [53.47785498477648]
  This paper investigates the problem of vehicle-cell association in millimeter wave (mmWave) communication networks. We first formulate the user state (VU) problem as a discrete non-vehicle association optimization problem. The proposed solution achieves up to 15% gains in terms sum of user complexity and 20% reduction in VUE compared to several baseline designs.
  arXiv  Detail & Related papers  (2020-01-22T08:51:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.