Guo Zihan, You Linlin, Zhu Rui, Zhang Yan, Yuen Chau
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China.
Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
Sci Data. 2025 Jul 17;12(1):1254. doi: 10.1038/s41597-025-05584-7.
With increasing policy and market support for electric vehicles (EVs) worldwide, analyzing EV charging demand is crucial for jointly optimizing transportation and energy systems. However, existing public datasets typically suffer from limited global coverage, coarse temporal resolution, and narrow feature availability. Here, we present CHARGED, a city-scale and harmonized dataset for global electric vehicle charging demand analysis. CHARGED contains hourly records from April 1 to September 30, 2023, covering about 12,000 charging chargers across six representative cities on six continents, including Amsterdam, Johannesburg, Los Angeles, Melbourne, São Paulo, and Shenzhen. Each entry encompasses core charging metrics (duration, volume, electricity price, and service price) alongside rich auxiliary information (weather variables, geospatial attributes, and multi-level static descriptors). CHARGED fills existing gaps and provides standardized data with spatiotemporal features aligned and multi-source information harmonized. Technical validation shows the potential of CHARGED to support in-depth characterization of user charging demand, and to impel the study of more advanced machine learning models, especially those enabling transfer learning across diverse urban contexts.
随着全球对电动汽车(EV)的政策和市场支持不断增加,分析电动汽车充电需求对于联合优化交通和能源系统至关重要。然而,现有的公共数据集通常存在全球覆盖范围有限、时间分辨率粗糙和特征可用性狭窄的问题。在此,我们展示了CHARGED,这是一个用于全球电动汽车充电需求分析的城市规模且统一的数据集。CHARGED包含2023年4月1日至9月30日的每小时记录,涵盖六大洲六个代表性城市的约12000个充电桩,包括阿姆斯特丹、约翰内斯堡、洛杉矶、墨尔本、圣保罗和深圳。每个条目包含核心充电指标(持续时间、电量、电价和服务价格)以及丰富的辅助信息(天气变量、地理空间属性和多级静态描述符)。CHARGED填补了现有空白,并提供了时空特征对齐且多源信息统一的标准化数据。技术验证表明,CHARGED有潜力支持对用户充电需求的深入表征,并推动对更先进机器学习模型的研究,特别是那些能够在不同城市环境中实现迁移学习的模型。