Yukta Pareek, Yasaman Masoudi
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.
@article{966ae3cd-c459-4eae-a7ee-d7b577a28a79,
title={Merging Large Language Models and Battery Physics for User-Aware Electric Vehicle Driving Management},
author={Yukta Pareek and Yasaman Masoudi},
year={2026},
language={English}
}TY - JOUR TI - Merging Large Language Models and Battery Physics for User-Aware Electric Vehicle Driving Management AU - Yukta Pareek AU - Yasaman Masoudi PY - 2026 LA - English ER -
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