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papersTODAY 04:00 UTC

LLM-Assisted Multi-Agent RL Framework Coordinates EV Charging, Stations and Grid

A new arXiv paper proposes combining large language models with multi-agent reinforcement learning to jointly optimize electric vehicle charging scheduling in public charging systems. The approach targets three competing goals at once: driver charging satisfaction, charging station profitability, and stability of the smart grid. It is positioned as a unified optimization method for connected EV infrastructure in IoT settings.

arXiv ev-charging internet-of-things large-language-models multi-agent-reinforcement-learning smart-grid

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arXiv cs.AILLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems ↗TODAY 04:00 UTC