A Digital Twin Framework for Decision-Support and Optimization of EV Charging Infrastructure in Localized Urban Systems

• URL: http://arxiv.org/abs/2510.24758v1
• Date: Tue, 21 Oct 2025 12:26:35 GMT
• Title: A Digital Twin Framework for Decision-Support and Optimization of EV Charging Infrastructure in Localized Urban Systems
• Authors: Linh Do-Bui-Khanh, Thanh H. Nguyen, Nghi Huynh Quang, Doanh Nguyen-Ngoc, Laurent El Ghaoui,
• Abstract summary: This study advances beyond static models by proposing a digital twin framework.<n>It integrates agent-based decision support with embedded optimization to dynamically simulate EV charging behaviors.<n>The model is applied to a localized urban site in Hanoi, Vietnam.
• Score: 0.6640968473398454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Electric Vehicle (EV) adoption accelerates in urban environments, optimizing charging infrastructure is vital for balancing user satisfaction, energy efficiency, and financial viability. This study advances beyond static models by proposing a digital twin framework that integrates agent-based decision support with embedded optimization to dynamically simulate EV charging behaviors, infrastructure layouts, and policy responses across scenarios. Applied to a localized urban site (a university campus) in Hanoi, Vietnam, the model evaluates operational policies, EV station configurations, and renewable energy sources. The interactive dashboard enables seasonal analysis, revealing a 20% drop in solar efficiency from October to March, with wind power contributing under 5% of demand, highlighting the need for adaptive energy management. Simulations show that real-time notifications of newly available charging slots improve user satisfaction, while gasoline bans and idle fees enhance slot turnover with minimal added complexity. Embedded metaheuristic optimization identifies near-optimal mixes of fast (30kW) and standard (11kW) solar-powered chargers, balancing energy performance, profitability, and demand with high computational efficiency. This digital twin provides a flexible, computation-driven platform for EV infrastructure planning, with a transferable, modular design that enables seamless scaling from localized to city-wide urban contexts.

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