Generative AI and LLMs Applied to Demand‑Side Management for Electric Vehicle Networks

Global: Generative AI and LLMs Applied to Demand‑Side Management for Electric Vehicle Networks

A team of researchers led by Hanwen Zhang and colleagues released a preprint on arXiv that describes a novel approach to integrating large language models (LLMs) with retrieval‑augmented generation for demand‑side management (DSM) in Internet‑of‑Electric‑Vehicle (IoEV) ecosystems. The work, first submitted on 26 January 2025 and revised through 26 December 2025, outlines how generative artificial intelligence can automate the formulation of optimization problems, generate code, and tailor solutions for IoT‑enabled microgrids.

Background on DSM and IoEV

Demand‑side management seeks to balance electricity consumption with supply, a challenge that grows as electric vehicles (EVs) become more prevalent and interconnected. When EVs are coordinated through IoV platforms, they can act as flexible loads or storage assets, but orchestrating charging schedules requires sophisticated, real‑time decision making.

LLMs as Intelligent Orchestrators

The authors argue that LLMs, trained on vast textual and code corpora, can interpret high‑level DSM objectives and translate them into executable optimization scripts. By augmenting LLMs with a retrieval component that accesses domain‑specific datasets and technical references, the system can produce context‑aware solutions without extensive manual programming.

Proposed Retrieval‑Augmented Framework

The paper proposes a pipeline that first captures user‑defined energy goals, then retrieves relevant mathematical models and constraints, and finally generates customized code for solving the resulting optimization problem. This automatic problem formulation and code synthesis aim to reduce the expertise barrier for operators of IoEV‑enabled microgrids.

Experimental Validation

Simulation results presented in the study demonstrate that the framework can schedule EV charging with a reported improvement of 12.4% in overall energy efficiency compared with baseline heuristics. The authors also note enhanced adaptability to fluctuating renewable generation and user preferences.

Implications and Future Directions

According to the authors, the findings highlight the potential of generative AI to streamline DSM workflows and support scalable deployment of smart charging infrastructures. They suggest that future work will explore real‑world field trials and the integration of additional IoT devices beyond EVs.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.