发表机构
The Hong Kong Polytechnic University; Illinois Institute of Technology（香港理工大学; 伊利诺伊理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对公共充电系统中电动汽车、充电站与电网的多目标冲突，提出LLM增强的MARL框架，利用大语言模型进行可解释特征选择与自适应权重分配，实现统一优化，显著提升市场效率并降低70%以上训练时间。
AI 中文摘要
在物联网（IoT）时代，协调联网电动汽车（EV）充电调度以平衡电动汽车充电满意度、充电站盈利能力和智能电网稳定性，构成了一项复杂的多目标挑战。现有的多智能体强化学习（MARL）方法往往难以应对由海量物联网传感数据产生的高维状态空间以及利益相关者之间的冲突利益。本文提出了一种新颖的LLM增强的MARL框架，首次在统一循环中同时优化电网、电动汽车和充电站。通过集成大语言模型（LLM），我们解决了两个关键瓶颈：可解释的特征选择和自适应的多目标平衡。LLM分析实时物联网收集的环境状态，提取具有物理意义的特征，并利用语义推理而非复杂的手动调参，动态地为冲突目标（包括利润、用户满意度和电网负载）分配权重。大量实验表明，我们的框架显著优于最先进的基线方法，在实现更高市场效率的同时，将训练时间减少了超过70%。该方法为高效、可持续的物联网赋能城市充电基础设施管理提供了一种可扩展、透明的解决方案。
英文摘要
In the era of the Internet of Things (IoT), coordinating connected electric vehicle (EV) charging scheduling to balance EV charging satisfaction, station profitability, and smart grid stability presents a complex multi-objective challenge. Existing Multi-Agent Reinforcement Learning (MARL) approaches often struggle with high-dimensional state spaces generated by massive IoT sensing data and conflicting stakeholder interests. This paper proposes a novel LLM-enhanced MARL framework that, for the first time, simultaneously optimizes the Grid, EVs, and Stations within a unified loop. By integrating Large Language Model (LLM), we address two critical bottlenecks: interpretable feature selection and adaptive multi-objective balancing. The LLM analyzes real-time IoT-collected environmental states to extract physically significant features and dynamically assigns weights to conflicting objectives-including profit, user satisfaction, and grid load-using semantic reasoning instead of complex manual tuning. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art baselines, achieving superior market efficiency while reducing training time by over 70%. This approach offers a scalable, transparent solution for efficient and sustainable IoT-enabled urban charging infrastructure management.
