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

2025-10-21

AI Summary

To address the multi-objective optimization challenge—balancing user satisfaction, energy efficiency, and financial viability—in urban electric vehicle (EV) charging infrastructure, this study proposes a digital twin framework integrating agent-based modeling with embedded optimization. The framework enables localized simulation of charging behavior, optimal spatial deployment of charging facilities, integration of renewable energy sources (e.g., solar and wind), and quantitative assessment of policy interventions. It features modularity and scalability, incorporating metaheuristic optimization algorithms, photovoltaic and wind power generation simulation, real-time data-driven dashboards, and seasonal load profiling. Validated in a university campus in Hanoi, the approach identified the optimal fast-charge/slow-charge station mix, improved user satisfaction by 20%, and significantly increased parking-space turnover via dynamic scheduling and idle-time surcharges. Results demonstrate the framework’s effectiveness and innovation in reconciling competing objectives and enabling scalable, city-level implementation.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Optimizing EV charging infrastructure for urban energy and user needs
• Developing a digital twin to simulate charging behaviors and policies
• Balancing renewable energy usage with charging demand efficiently

Innovation

• Digital twin framework integrates agent-based decision support
• Embedded metaheuristic optimization identifies near-optimal charger mixes
• Modular design enables seamless scaling from local to city-wide