ArticleScientific data2026
An AI-Augmented Dataset of Multi-Prototype Electric Vehicle Charging Load Profiles in China.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- CBAM-YOLOv11 and Geometric Constraint-Enhanced PnP for High-Precision EV Charging Port Pose Estimation.Sensors (Basel, Switzerland) · 2026Article
- An AI-Augmented Dataset of Multi-Prototype Electric Vehicle Charging Load Profiles in China.Scientific data · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large-scale electric vehicle integration poses significant challenges to power grid operation, demanding high-fidelity and diversified datasets for in-depth research. To address this need, we introduce MP-EVData, a comprehensive dataset of station-level charging load profiles from a major Chinese metropolis in 2024. The core value of MP-EVData is providing charging load data for 10 stations in the same geographical location during the same time period, representing five distinct prototypes: taxi demonstration stations, bus depots, residential charging stations, battery swapping stations and heavy-duty truck stations. This unique structure eliminates the disturbances of external variables such as geographical, climatic, and policy, enabling controlled 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 researches in load forecasting, smart charging algorithms and urban infrastructure planning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.