AI 中文总结
本文提出融合大语言模型与电池物理的混合框架，通过LLM解析用户意图生成有界行为参数，并经物理安全过滤器约束，实现安全且用户感知的电动汽车驾驶管理。
AI 中文摘要
电动汽车（EV）电池性能与驾驶员行为密切相关，然而人类意图通常以语义而非数值形式表达。本文提出一种混合物理-人工智能框架，将大语言模型（LLM）作为高层行为推理层集成到物理驱动的监督架构中。LLM 解析文本用户意图和结构化电池反馈，生成有界行为参数，从而塑造放电电流包络。随后，物理驱动的安全过滤器在计算可行的速度建议之前强制执行物理安全约束。基于李雅普诺夫的分析建立了在电池模型和提示不准确情况下的有界建议误差。仿真结果表明，该框架在不损害物理安全的前提下实现了自适应、用户感知的运行。所提出的推理-执行架构为电动汽车能量管理中安全集成人工智能提供了一条原则性路径。
英文摘要
Electric vehicle (EV) battery performance is strongly coupled with driver behavior, yet human intent is typically expressed semantically rather than numerically. This paper proposes a hybrid physics-artificial intelligence framework that integrates a Large Language Model (LLM) as a high-level behavioral reasoning layer within a physics-driven supervisory architecture. The LLM interprets textual user intent and structured battery feedback to generate bounded behavioral parameters that shape a discharge current envelope. A physics-driven safety filter then enforces physical safety constraints before computing feasible velocity recommendations. Lyapunov-based analysis establishes bounded recommendation error under battery model and prompt inaccuracies. Simulation results demonstrate adaptive, user-aware operation without compromising physical safety. The proposed reasoning-enforcement architecture provides a principled pathway for safe AI integration in EV energy management.
