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

• URL: http://arxiv.org/abs/2609.13805v1
• Date: Sat, 12 Sep 2026 08:33:15 GMT
• Title: LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems
• Abstract summary: Multi-Agent Reinforcement Learning (MARL) approaches often struggle with high-dimensional state spaces generated by massive IoT sensing data and conflicting stakeholder interests.<n>This paper proposes a novel LLM-enhanced framework that simultaneously optimize the Grid, EVs, and Stations within a unified loop.<n>Our framework significantly outperforms state-of-the-art baselines, achieving superior market efficiency while reducing training time by over 70%.
• Score: 11.390254836910977
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Driving [26.33691640001489]
  This paper proposes a large language model (LLM) enhanced differentiable trajectory planning framework for IoT-enabled autonomous driving.<n>We introduce a surrounding agent centric data augmentation strategy to reorganize sur rounding agent trajectories as additional planning supervision.<n>We also design a complexity-aware asyn chronous LLM-based semantic enhancement module to extract scene-related high-level semantic features.
  arXiv  Detail & Related papers  (2026-07-11T18:43:14Z)
• Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization [61.55616421408666]
  Low-Altitude Economy Networks (LAENets) have enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.<n> onboard vision (VLMs) offer inference for real-time inference but limited onboard dynamic network conditions.<n>We propose a UAV-enabled LAENet system that improves communication efficiency under dynamic LAENet conditions.
  arXiv  Detail & Related papers  (2025-10-11T05:11:21Z)
• CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks [57.95170323315603]
  We introduce CollaPipe, a distributed learning framework that integrates collaborative pipeline parallelism with federated aggregation to support self-evolving networks.<n>In CollaPipe, the encoder part is adaptively partitioned into variable-sized segments and deployed across mobile devices for pipeline-parallel training, while the decoder is deployed on edge servers to handle generative tasks.<n>To enhance training efficiency, we formulate a joint optimization problem that adaptively allocates model segments, micro-batches, bandwidth, and transmission power.
  arXiv  Detail & Related papers  (2025-09-24T07:54:01Z)
• Decentralized Rank Scheduling for Energy-Constrained Multi-Task Federated Fine-Tuning in Edge-Assisted IoV Networks [5.034703123469061]
  In Internet of Vehicles (IoV) systems, enabling efficient and low-latency multi-task adaptation is challenging due to client mobility, heterogeneous resources, and intermittent connectivity.<n>This paper proposes a hierarchical federated fine-tuning framework that coordinates roadside units (RSUs) and vehicles to support resource-aware and mobility-resilient learning across dynamic IoV scenarios.
  arXiv  Detail & Related papers  (2025-08-13T06:29:00Z)
• SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving [51.47621083057114]
  SOLVE is an innovative framework that synergizes Vision-Language Models with end-to-end (E2E) models to enhance autonomous vehicle planning.<n>Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive interaction between VLM and E2E components.
  arXiv  Detail & Related papers  (2025-05-22T15:44:30Z)
• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• Open-Source LLM-Driven Federated Transformer for Predictive IoV Management [1.8024397171920885]
  Federated Prompt-d Traffic Transformer (FPoTT) is a novel framework that leverages open-source Large Language Models for predictive IoV management.<n>FPoTT introduces a dynamic prompt optimization mechanism that iteratively refines textual prompts to enhance trajectory prediction.<n>The architecture employs a dual-layer federated learning paradigm, combining lightweight edge models for real-time inference with cloud-based LLMs to retain global intelligence.
  arXiv  Detail & Related papers  (2025-05-01T16:54:21Z)
• Intelligent Task Offloading in VANETs: A Hybrid AI-Driven Approach for Low-Latency and Energy Efficiency [2.1877558143992184]
  Vehicular Ad-hoc Networks (VANETs) are integral to intelligent transportation systems.<n>VANETs enable vehicles to offload computational tasks to nearby roadside units (RSUs) and mobile edge computing (MEC) servers for real-time processing.<n>This research proposes a hybrid AI framework that integrates supervised learning, reinforcement learning, and Particle Swarm Optimization (PSO) for intelligent task offloading and resource allocation.
  arXiv  Detail & Related papers  (2025-04-29T13:20:02Z)
• Smooth Handovers via Smoothed Online Learning [48.953313950521746]
  We first analyze an extensive dataset from a commercial mobile network operator (MNO) in Europe with more than 40M users, to understand and reveal important features and performance impacts on HOs.<n>Our findings highlight a correlation between HO failures/delays, and the characteristics of radio cells and end-user devices.<n>We propose a realistic system model for smooth and accurate HOs that extends existing approaches by incorporating device and cell features on HO optimization.
  arXiv  Detail & Related papers  (2025-01-14T13:16:33Z)
• Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning [52.64813150003228]
  The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring.<n>In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas.<n>The task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV.
  arXiv  Detail & Related papers  (2025-01-11T02:32:42Z)
• DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities [5.78463306498655]
  Electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and jeopardized stability. Modern power grids require coordinated or smart'' charging strategies capable of optimizing EV charging scheduling in a scalable and efficient fashion. We formulate a time-coupled binary optimization problem that maximizes EV users' total welfare gain while accounting for the network's available power capacity and stations' occupancy limits.
  arXiv  Detail & Related papers  (2023-05-18T14:03:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.