A Data-Driven Framework for Electric Vehicle Charging Infrastructure Planning: Demand Estimation, Economic Feasibility, and Spatial Equity

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities.

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Publication Details

Journal
World Electric Vehicle Journal
Published
2026-01-14
DOI
https://doi.org/10.3390/wevj17010042
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.00

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article

A Data-Driven Framework for Electric Vehicle Charging Infrastructure Planning: Demand Estimation, Economic Feasibility, and Spatial Equity

May EI Barachi, Zina Abohaia, Mahmoud Shaat, Farhad Oroumchian
World Electric Vehicle Journal
Electric Vehicles and Infrastructure
article

A Data-Driven Framework for Electric Vehicle Charging Infrastructure Planning: Demand Estimation, Economic Feasibility, and Spatial Equity

May EI Barachi, Zina Abohaia, Mahmoud Shaat, Farhad Oroumchian
article en

Abstract

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities.

World Electric Vehicle JournalVol. 17(1)
University of Wollongong in Dubai (AE)
U.S. Department of Energy
Industry, innovation and infrastructure
Openalex Percentile: Top 88%
Electric Vehicles and Infrastructure
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A Data-Driven Framework for Electric Vehicle Charging Infrastructure Planning: Demand Estimation, Economic Feasibility, and Spatial Equity — May EI Barachi, Zina Abohaia, et al. · World Electric Vehicle Journal (2026) | TGRS Research Map | TGRS