Optimizing Electric Vehicle Charging Station Locations: A Data-driven System with Multi-source Fusion

2025-04-18

AI Summary

Urban electric vehicle (EV) charging infrastructure planning faces challenges including range anxiety and inadequate coverage of residential areas. Method: This study proposes an interpretable, geospatially informed siting methodology integrating multi-source geographic and spatiotemporal data. For the first time, it incorporates natural disaster risk (e.g., fire and flood) and point-of-interest (POI) functional attributes into the geographic feasibility assessment framework for charging stations, enabling joint modeling of demand prediction and spatial constraints. Leveraging real-world EV trip data, road networks, administrative boundaries, and hazard maps from New South Wales, the approach combines GIS analysis, hotspot detection, and empirically grounded demand modeling to generate a high-feasibility candidate site atlas. Contribution/Results: Applied to the Sydney metropolitan area, the recommended sites cover 87% of high-demand unserved road segments, substantially mitigating range anxiety and establishing a novel paradigm for resilient, low-carbon transportation infrastructure planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: Localization, Mapping, and Navigation

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: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

With the growing electric vehicles (EVs) charging demand, urban planners face the challenges of providing charging infrastructure at optimal locations. For example, range anxiety during long-distance travel and the inadequate distribution of residential charging stations are the major issues many cities face. To achieve reasonable estimation and deployment of the charging demand, we develop a data-driven system based on existing EV trips in New South Wales (NSW) state, Australia, incorporating multiple factors that enhance the geographical feasibility of recommended charging stations. Our system integrates data sources including EV trip data, geographical data such as route data and Local Government Area (LGA) boundaries, as well as features like fire and flood risks, and Points of Interest (POIs). We visualize our results to intuitively demonstrate the findings from our data-driven, multi-source fusion system, and evaluate them through case studies. The outcome of this work can provide a platform for discussion to develop new insights that could be used to give guidance on where to position future EV charging stations.

Problem

• Optimizing EV charging station locations using multi-source data
• Addressing range anxiety and residential charging distribution issues
• Developing a data-driven system for demand estimation and deployment

Innovation

• Data-driven system for EV charging optimization
• Multi-source fusion including trip and geographical data
• Visualization and evaluation through case studies