A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand Analysis
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05584-7 |
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| author | Zihan Guo Linlin You Rui Zhu Yan Zhang Chau Yuen |
| author_facet | Zihan Guo Linlin You Rui Zhu Yan Zhang Chau Yuen |
| author_sort | Zihan Guo |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-2178242843384156a60d33d45fdf3dcf |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-2178242843384156a60d33d45fdf3dcf2025-08-20T03:42:47ZengNature PortfolioScientific Data2052-44632025-07-0112111210.1038/s41597-025-05584-7A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand AnalysisZihan Guo0Linlin You1Rui Zhu2Yan Zhang3Chau Yuen4School of Intelligent Systems Engineering, Sun Yat-sen UniversitySchool of Intelligent Systems Engineering, Sun Yat-sen UniversityInstitute of High-Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)Department of Informatics, University of OsloSchool of Electrical and Electronics Engineering, Nanyang Technological UniversityAbstract 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.https://doi.org/10.1038/s41597-025-05584-7 |
| spellingShingle | Zihan Guo Linlin You Rui Zhu Yan Zhang Chau Yuen A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand Analysis Scientific Data |
| title | A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand Analysis |
| title_full | A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand Analysis |
| title_fullStr | A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand Analysis |
| title_full_unstemmed | A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand Analysis |
| title_short | A City-scale and Harmonized Dataset for Global Electric Vehicle Charging Demand Analysis |
| title_sort | city scale and harmonized dataset for global electric vehicle charging demand analysis |
| url | https://doi.org/10.1038/s41597-025-05584-7 |
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