MP-EVData：An AI-Augmented Dataset of Multi-Prototype Electric Vehicle Charging Load Profiles in China

The large-scale integration of electric vehicles (EVs) presents significant challenges to power grid stability, necessitating high-quality, diverse charging datasets for effective modeling and management. To address this need, we introduce MP-EVData, a comprehensive dataset of station-level charging load profiles from a major Chinese metropolis, covering the full 2024 calendar year. Its core innovation lies in providing co-located and co-temporal data for 10 stations representing five distinct prototypes: taxi demonstration stations, bus depots, residential charging stations, battery swapping stations, and heavy-duty truck stations. This unique structure eliminates confounding geographical, climatic, and policy variables, enabling direct comparative analysis of their load characteristics. Furthermore, the dataset is augmented with a parallel, high-fidelity synthetic dataset generated using advanced generative AI models to support data-intensive research. Technical validation reveals highly distinct daily, weekly, and annual temporal patterns across prototypes and demonstrates clear price-responsive charging behavior under time-of-use pricing. MP-EVData provides a crucial benchmark for advancing research in load forecasting, smart charging algorithms, and urban infrastructure planning.