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<p>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.</p>

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A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand Analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月17日

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  • Zihan Guo,
  • Linlin You,
  • Rui Zhu,
  • Yan Zhang,
  • Chau Yuen

摘要

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.