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

LLM增强的MARL优化电动汽车充电系统

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一个新框架，该框架使用大型语言模型（LLMs）来增强多智能体强化学习（MARL），以优化电动汽车充电系统。该方法通过使LLMs能够从物联网数据中选择重要特征，并动态平衡利润、用户满意度和电网负荷等优先级，从而解决了高维状态空间和冲突目标带来的挑战。实验表明，这种统一的循环在市场效率方面显著优于现有方法，并减少了70%以上的训练时间。 AI

影响这项研究为管理复杂的电动汽车充电基础设施提供了一个可扩展且透明的解决方案，有望提高城市环境的效率和可持续性。

排序理由该集群包含一篇详细介绍新颖框架和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yang Zhang, Lindong Xie, Chongyu Wang, Gaojunjie Li, Siqi Bu, Edward Chung · 2026-09-15 04:00

面向公共充电系统中电动汽车-充电站-电网统一优化的LLM增强多智能体强化学习

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 cha…

本文如何被排名

报道来源 [1]

面向公共充电系统中电动汽车-充电站-电网统一优化的LLM增强多智能体强化学习

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