# Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks

arXiv: https://arxiv.org/abs/2502.03067 · HTML: https://mlsanity.com/paper/2502.03067/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap](https://mlsanity.com/paper/2503.21411/) (2025)
- [Context-Aware Agentic Power Resources Optimisation in EV using Smart2ChargeApp](https://mlsanity.com/paper/2512.12048/) (2025)

## Abstract (arXiv)

Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL) approaches often struggle with the high dimensionality and dynamic nature of real-time EV charging, leading to sub-optimal solutions. To address these challenges, this study demonstrates that combining Large Language Models (LLMs), for sequence modeling, with Graph Neural Networks (GNNs), for relational information extraction, not only outperforms conventional EV smart charging methods, but also paves the way for entirely new research directions and innovative solutions.