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 dynamic 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.

翻译：随着电动汽车在城市环境中的普及加速，优化充电基础设施对于平衡用户满意度、能源效率和经济可行性至关重要。本研究超越静态模型，提出一种融合基于智能体的决策支持与内嵌优化的数字孪生框架，以动态模拟跨场景下的电动汽车充电行为、基础设施布局及政策响应。该模型应用于越南河内一处城市局部区域（大学校园），评估运营政策、充电站配置及可再生能源方案。交互式仪表盘可实现季节性分析，揭示十月至三月太阳能效率降低20%，其中风电贡献不足5%的需求，凸显了自适应能源管理的必要性。模拟结果表明，动态通知新增可用充电位可提升用户满意度，而燃油车禁入与空闲占位费制度以极低的附加复杂性提高了充电位周转率。内嵌元启发式优化能够识别出快速（30千瓦）与标准（11千瓦）太阳能充电桩的近最优组合，在高计算效率下平衡能源性能、盈利能力与需求。该数字孪生为电动汽车基础设施规划提供了灵活、计算驱动的平台，其模块化可迁移设计支持从局部到城市范围的顺畅扩展。