Modeling electric vehicle behavior: Insights from long-term charging and energy consumption patterns through empirical trajectory data

Qing Yu, Jiaxing Li, Defan Feng, Xuanyu Liu, Jian Yuan, Haoran Zhang and Xin Wang

Applied Energy, 2025, vol. 380, issue C, No S0306261924024504

Abstract: Understanding electric vehicle (EV) charging demand and user behavior is crucial for effective infrastructure design. Existing studies on electric vehicle charging patterns have faced challenges due to limited data access and insufficient long-term monitoring. This study utilizes empirical EV trajectory data to reveal long-term patterns in charging behavior, energy consumption, and potential demand. By classifying EVs based on type, infrastructure availability, and usage patterns, we analyze their spatiotemporal features using probabilistic distribution models and provide standardized daily profiles for different EV clusters. Our findings show that EV charging behavior is fairly regular, with higher consistency among users with greater charging reliance. Accessibility of charging facilities at home or work significantly affects charging behavior. There is a notable gap between potential and actual charging demand in city, indicating significant potential for daytime charging. Future strategies could focus on placing charging infrastructure to guide charging behavior to manage urban demand effectively, enhancing infrastructure resilience and achieving peak shaving and valley filling.

Keywords: Electric vehicle; Charging behavior; Energy consumption; Regularity measurement; Probabilistic distribution models (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.125066

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