Problem
Research questions and friction points this paper is trying to address.
Electric Vehicle (EV)
Multi-Agent Reinforcement Learning (MARL)
Internet of Things (IoT)
Grid Stability
Charging Scheduling
LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems
🤖 AI Summary
本文提出一种LLM增强的MARL框架，通过语义推理动态平衡电网、电动车和充电站之间的多目标冲突，提高系统效率并减少训练时间。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-enhanced MARL
unified optimization
adaptive multi-objective balancing
interpretable feature selection
semantic reasoning
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