用大模型动态推荐充电桩，解决充电难问题
LLM-Enabled EV Charging Stations Recommendation
- 基于大模型和自然语言推理，实时整合多源数据
- 通过提示工程测试，显著提升推荐准确性和效率
- 适合关注智能交通与个性化服务的开发者
充电基础设施的建设速度难以跟上电动汽车（EV）使用量的增长，导致车主面临长时间等待、续航焦虑和整体不满。现有推荐系统因数据碎片化以及位置、电价、用户偏好等因素整合复杂而效果不佳。为此，我们提出RecomBot——一种基于大语言模型（LLM）的提示驱动推荐系统，可动态推荐最优充电站（CS），利用实时异构数据实现更精准的个性化推荐。通过在多种提示工程场景下的测试，结果表明该模型具备高效性与可扩展性，能适应不同车型，显著提升充电效率与用户体验。
原文摘要 · Abstract (English)
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.
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