Imagine telling your electric vehicle, "Charge by 6 a.m. tomorrow, but keep an eye on my battery health and electricity costs." Now, imagine your car actually understands and optimizes its charging schedule based on your exact needs. This futuristic scenario is closer than you think, thanks to the power of large language models (LLMs). Researchers are exploring how LLMs can bridge the gap between human language and the complex world of power scheduling. Traditionally, power grids operate on fixed algorithms, oblivious to individual preferences. But LLMs offer a way to personalize energy management, allowing users to express their needs in natural language. This new research introduces a multi-agent LLM system that acts as a "VRQ2Vec" converter. It takes a user's voice request (VRQ) and translates it into a power consumption schedule (Vec). This system uses three specialized LLM agents. The first one identifies the user's intent, like minimizing cost or charging time. The second agent extracts key parameters from the request, such as deadlines or battery level targets. Finally, the third agent solves the optimization problem, delivering a tailored power schedule. Early tests using Llama 3 8B show promising results. While a larger vocabulary of optimization problems can improve flexibility, it also increases the risk of misinterpretation. Researchers found that providing more context specific to electric vehicle charging significantly improves the LLM's accuracy. Future work will focus on refining the system, exploring other LLM architectures like GPT-4, and expanding the capabilities to other domains like smart homes and wireless networks. This research opens up exciting possibilities for user-centric control of complex systems, making technology more intuitive and responsive to our individual needs.