LLM-Enabled EV Charging Stations Recommendation

TL;DR

This paper introduces RecomBot, an LLM-powered recommender system that dynamically suggests optimal EV charging stations by integrating real-time heterogeneous data, enhancing personalization and efficiency in EV charging infrastructure.

Contribution

The paper presents a novel LLM-based prompt system, RecomBot, that improves EV charging station recommendations by handling complex data and personalizing suggestions.

Findings

RecomBot effectively integrates real-time data for EV station recommendations.

The system enhances personalization and charging efficiency.

Testing shows RecomBot's capability and adaptability across scenarios.

Abstract

Charging infrastructure is not expanding quickly enough to accommodate the increasing usage of Electric Vehicles (EVs). For this reason, EV owners experience extended waiting periods, range anxiety, and overall dissatisfaction. Challenges, such as fragmented data and the complexity of integrating factors like location, energy pricing, and user preferences, make the current recommendation systems ineffective. To overcome these limitations, we propose RecomBot, which is a Large Language Model (LLM)-powered prompt-based recommender system that dynamically suggests optimal Charging Stations (CSs) using real-time heterogeneous data. By leveraging natural language reasoning and fine-tuning EV-specific datasets, RecomBot enhances personalization, improves charging efficiency, and adapts to various EV types, offering a scalable solution for intelligent EV recommendation systems. Through testing across various prompt engineering scenarios, the results obtained underline the capability and efficiency of the proposed model.