[2408.05233] Electric Vehicle User Charging Behavior Analysis Integrating Psychological and Environmental Factors: A Statistical-Driven LLM based Agent Approach

Computer Science > Artificial Intelligence arXiv:2408.05233 (cs) [Submitted on 3 Aug 2024 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Electric Vehicle User Charging Behavior Analysis Integrating Psychological and Environmental Factors: A Statistical-Driven LLM based Agent Approach Authors:Chuanlin Zhang, Junkang Feng, Chenggang Cui, Pengfeng Lin, Hui Chen, Yan Xu, A. M. Y. M. Ghias, Qianguang Ma, Pei Zhang View a PDF of the paper titled Electric Vehicle User Charging Behavior Analysis Integrating Psychological and Environmental Factors: A Statistical-Driven LLM based Agent Approach, by Chuanlin Zhang and 8 other authors View PDF HTML (experimental) Abstract:With the growing adoption of electric vehicles (EVs), understanding user charging behavior has become critical for grid stability and transportation planning. This study investigates the behavioral heterogeneity of EV taxi drivers by analyzing the interaction between psychological traits and situational triggers within dynamic travel contexts. Leveraging large language models (LLMs) as a core simulation tool, a novel framework with statistical enhancement is developed to replicate and analyze the charging behaviors of taxi drivers. LLMs simulate personalized decision-making processes by leveraging natural language reasoning and role-playing capabilities, accounting for factors such as time sensitivity, price awareness, and range anxiety. Simulation results indicate that the framework reliably reproduces rea...