LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems

A new arXiv paper (arXiv:2609.13805v1) proposes an LLM-enhanced multi-agent reinforcement learning framework that simultaneously optimizes the grid, electric vehicles, and charging stations within a unified loop for public charging systems. The framework uses a large language model to extract physically significant features from real-time IoT-collected environmental states and to dynamically assign weights to conflicting objectives including profit, user satisfaction, and grid load via semantic reasoning. The authors report that the framework outperforms state-of-the-art baselines while reducing training time by over 70%.

arXiv:2609.13805v1 Announce Type: new 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.