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Auxiliary Euro-Calliope datasets: Spatio-temporal data representing national cooking demand and electric vehicle characteristic profiles in Europe
<p>Output generated by the <a href="https://github.com/RAMP-project/">RAMP engine</a> for use in the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope model</a>. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope.</p> <ul> <li>Cooking energy demand profiles (<em>ramp-cooking-profiles</em>): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP">RAMP model</a> [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**.</li> <li>Electric vehicle plug-in profiles (<em>ramp-ev-plugin-profiles</em>): Profiles of the percentage of parked electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*.</li> <li>Electric vehicle energy consumption profiles (<em>ramp-ev-consumption-profiles</em>): Profiles of the electricity consumption of electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*.</li> </ul> <p>* AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE</p> <p>** (*) + BGR, SRB</p> <p>*** ALB, MKD, GRC, CYP, BIH, MNE, ISL</p> <p>[1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. <em>Energy</em> 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097.</p> <p>[2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. <em>Applied Energy</em> 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676.</p>
Bidirectional and Unidirectional Charging Profiles of Electric Vehicles
<p>This dataset contains bidirectional and unidirectional charging profiles of Electric Vehicles (EVs) measured in laboratory environment at the Smart Grid Technology Lab of ie³ institute at TU Dortmund University. The dataset not only considers charging power and current but also harmonics/interharmonics emission of EV charging in both static and dynamic scenarios. Thus, it provides a solid foundation for the development of advanced EV charging algorithms and model validation. Raw data are available in csv format from the file <em>dataset_raw.zip</em> and a selection of merged measurements is provided in the file <em>dataset_merged.zip</em>.</p> <p>The following commercially available EV models are considered:</p> <ul> <li>Opel Corsa-e (2020)</li> <li>Fiat 500e (2022)</li> <li>Honda-e Advance (bidirectional, 2020)</li> <li>Nissan Leaf (bidirectional, 2020)</li> <li>VW ID.4 (2020)</li> <li>Hyundai Ioniq 5 (2021)</li> <li>Mitsubishi Eclipse Cross PHEV (bidirectional, 2022)</li> <li>Tesla Model Y SR (2022)</li> </ul> <p>The dataset is part of the deliverable D8.1 of DriVe2X project and is accompanied by a report including a description about data acquisition and measurement setup. The report is available from the project website's resources section. A more in-depth description of the tests and exemplary analysis is currently being prepared for publication.</p> <p><strong>References</strong></p> <ul> <li>DriVe2X project website: <a href="https://drive2x.eu/">Link</a></li> <li>CORDIS website: <a href="https://cordis.europa.eu/project/id/101056934">Link</a></li> <li>ie³ institute: <a href="https://ie3.etit.tu-dortmund.de/">Link</a></li> <li>Smart Grid Technology Lab: <a href="http://sgtl.et.tu-dortmund.de/">Link</a></li> </ul>
Data files: Electric vehicle charging dataset with 35,000 charging sessions from 12 residential locations in Norway
<p>Please refer to the data article where the data is described (Data-in-brief, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110883" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dib.2024.110883</span></span></a>).</p> <p>The data article refers to the paper "A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study". The Electric Vehicle (EV) charging dataset includes detailed information on plug-in times, plug-out times, and energy charged for over 35,000 residential charging sessions, covering 267 user IDs across 12 locations within a mature EV market in Norway. Utilising methodologies outlined in the paper, realistic predictions have been integrated into the datasets, encompassing EV battery capacities, charging power, and plug-in State-of-Charge (SoC) for each EV-user and charging session. In addition, hourly data is provided, such as energy charged and connected energy capacity for each charging session.</p> <p>The comprehensive dataset provides the basis for assessing current and future EV charging behaviour, analysing and modelling EV charging loads and energy flexibility, and studying the integration of EVs into power grids.</p>
Electric Vehicle Usage and Charging Analysis Dataset Across Seven Major Cities in China
<div> <h1><strong>Background </strong></h1> </div> <div> <p>This dataset provides supporting data for the figures presented in our study on electric vehicle (EV) usage and charging behavior across major Chinese cities. The detailed analysis and raw data are thoroughly described in Zhan et al (2025). The study examines 1.69 million EVs, representing 42% of China's total EV fleet, from November 2020 to October 2021. The study provides insights into operational demands, infrastructure requirements, and energy consumption patterns by analyzing diverse vehicle types—including private cars, taxis, buses, and special purpose vehicles (SPVs). </p> </div> <div> <p>The purpose of this dataset is to enable researchers who do not have access to the same raw data to replicate, calibrate, or extend our findings using the processed data that underpins each figure. This resource is valuable for further research on EV infrastructure planning, energy consumption, and vehicle performance. This dataset is made available to help the research community leverage our findings and facilitate advancements in electric vehicle research and infrastructure planning. Please refer to Zhan et al (2025) for full details on the methodology and analysis. </p> </div> <div> <p> </p> <h1><strong>Data description </strong></h1> </div> <div> <p>This dataset includes the processed data underlying each figure in Zhan et al (2025), covering various aspects of EV usage, battery capacity, and charging behavior across seven major Chinese cities: Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, Chengdu, and Chongqing. The dataset is organized to correspond directly with the figures in the paper, facilitating its use for further analysis and model calibration. Each dataset is aligned with specific figures, providing essential data to help researchers without access to the original raw data. </p> </div> <div> <p> </p> <h2><strong>1. EV Type and Battery Energy Distribution Across Cities</strong></h2> </div> <div> <p><strong>Fig1a.Distribution of EV types across selected Chinese cities </strong></p> </div> <div> <p>File: Fig1a.Distribution of EV types across selected Chinese cities.csv </p> </div> <div> <p>Description: Distribution of EV types across seven cities, detailing the share of different vehicle types. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Beijing </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Beijing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shenzhen </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Shenzhen </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shanghai </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Shanghai </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Guangzhou </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Guangzhou </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chengdu </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Chengdu </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chongqing </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Chongqing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Nanjing </p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Nanjing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig1b.Distribution of battery energy by vehicle types </strong></p> </div> <div> <p>File: Fig1b.Distribution of battery energy by vehicle types.csv </p> </div> <div> <p>Description: Distribution of battery energy across different vehicle types, represented as box plot statistics. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type_2 </p> </div> </div> </td> <td> <div> <div> <p>vehicle types </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>2. Variations in Battery Energy</strong></h2> </div> <div> <p><strong>Fig1c.Variations of battery energy of buses </strong></p> </div> <div> <p>File: Fig1c.Variations of battery energy of buses across studied cities.csv </p> </div> <div> <p>Description: Battery energy variations for buses across the studied cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_En </p> </div> </div> </td> <td> <div> <div> <p>English name of 7 Chinese city </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy of buses corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of battery energy of buses. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of battery energy of buses. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of battery energy of buses. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy of buses corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig1d.Variations of battery energy of SPVs </strong></p> </div> <div> <p>File: Fig1c.Variations of battery energy of SPVs across studied cities.csv </p> </div> <div> <p>Description: Battery energy variations for special purpose vehicles (SPVs) across cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_En </p> </div> </div> </td> <td> <div> <div> <p>English name of 7 Chinese city </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy of SPVs corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of battery energy of SPVs. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of battery energy of SPVs. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of battery energy of SPVs. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The battery energy of SPVs corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>3. Daily Driving Distance and Energy Consumption</strong></h2> </div> <div> <p><strong>Fig1e.Daily driving distance of different vehicle types </strong></p> </div> <div> <p>File: Fig1e.Daily driving distance of different vehicle types.csv </p> </div> <div> <p>Description: Cumulative distribution functions (CDFs) of daily driving distances for various vehicle types. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>CDF Percentile </p> </div> <div> <p> </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Private car </p> </div> </div> </td> <td> <div> <div> <p>The value of private car daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Official car </p> </div> </div> </td> <td> <div> <div> <p>The value of official car daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>SPV </p> </div> </div> </td> <td> <div> <div> <p>The value of SPV daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Rental car </p> </div> </div> </td> <td> <div> <div> <p>The value of rental car daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Bus </p> </div> </div> </td> <td> <div> <div> <p>The value of bus daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Taxi </p> </div> </div> </td> <td> <div> <div> <p>The value of taxi daily driving distance corresponding to CDF Percentile </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig1f-1. Ratio of daily energy consumed over battery energy </strong></p> </div> <div> <p>File: Fig1f-1.The ratio of daily energy consumed over battery energy.csv </p> </div> <div> <p>Description: Ratio of daily energy consumption relative to battery energy for each vehicle type. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type_2 </p> </div> </div> </td> <td> <div> <div> <p>vehicle types </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The energy ratio corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of energy ratio. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of energy ratio. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of energy ratio. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The energy ratio corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig1f-2. Number of charging events per day</strong></p> </div> <div> <p>File: Fig1f-2.The number of charging events per day.csv </p> </div> <div> <p>Description: Data on the number of daily charging events across vehicle types. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type_2 </p> </div> </div> </td> <td> <div> <div> <p>vehicle types </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The charging events per day corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of charging events per day. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of charging events per day. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of charging events per day. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The charging events per day corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>4. EV Usage Patterns and State of Charge (SOC)</strong></h2> </div> <div> <p><strong>Fig2a.Daily usage patterns of EVs </strong></p> </div> <div> <p>File: Fig2a.Daily usage patterns of EVs across different vehicle types and days.csv </p> </div> <div> <p>Description: Usage patterns of EVs by type and day, segmented into 15-minute intervals. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Vehicle type_day type_state </p> </div> </div> </td> <td> <div> <div> <p>Take Private car_workday_driving as an example, it refers to the ratio of private cars parked to the total number of private cars on weekdays within a 15-minute period </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig2b. SOC levels before and after charging </strong></p> </div> <div> <p>File: Fig2b. SOC levels before and after charging by charging level by vehicle type.csv </p> </div> <div> <p>Description: SOC levels before and after charging events, classified by charging level and vehicle type. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>vehicle_SOC_P </p> </div> </div> </td> <td> <div> <div> <p>Take Private car_Start SOC_P1 as an example, it refers to SOC of private cars charging with P1 at the start of charging </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The SOC corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of SOC. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of SOC. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of SOC. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The SOC corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>5. Energy Consumption Rate (ECR) of Passenger Cars</strong></h2> </div> <div> <p><strong>Fig2c-top. ECR of passenger cars by month of the year </strong></p> </div> <div> <p>File: Fig2c-top.Energy consumption rate (ECR) of passenger cars by month of the year.csv </p> </div> <div> <p>Description: Monthly ECR of passenger cars in different cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Beijing </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Beijing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shenzhen </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Shenzhen </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shanghai </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Shanghai </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Guangzhou </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Guangzhou </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chengdu </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Chengdu </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chongqing </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Chongqing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Nanjing </p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Nanjing </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig2c-bottom.ECR of passenger cars as a function of temperature </strong></p> </div> <div> <p>File: Fig2c-bottom.ECR of passenger cars as a function of temperature.csv </p> </div> <div> <p>Description: Passenger vehicle ECR in relation to temperature across different cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Temperature </p> </div> </div> </td> <td> <div> <div> <p>Temperature of a city in a certain month </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>℃ </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>ECR </p> </div> </div> </td> <td> <div> <div> <p>Average energy consumption rate of passenger cars of a city in a certain month </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kWh/100km </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>6. Charging Events and Load Distribution</strong></h2> </div> <div> <p><strong>Fig3-1.Number of vehicles being charged by level by time of day </strong></p> </div> <div> <p>File: Fig3-1.Number of vehicles being charged by level by time of day.csv </p> </div> <div> <p>Description: Number of vehicles charging at different power levels throughout the day. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Vehicle type_P_day type </p> </div> </div> </td> <td> <div> <div> <p>Take Private car_P1_workday as an example, it refers to number of private cars being charged with P1 on weekdays within a 5-minute period </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>Fig3-2.Daily charging load from electric vehicles </strong></p> </div> <div> <p>File: Fig3-2.Daily charging load from electric vehicles across different vehicle types and power level.csv </p> </div> <div> <p>Description: Charging load data across vehicle types and power levels, aggregated by time of day. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Vehicle type_P_day type </p> </div> </div> </td> <td> <div> <div> <p>Take Private car_P1_workday as an example, it refers to charging load of private cars being charged with P1 on weekdays within a 5-minute period </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>7. Spatial Distribution of Max Charging Power </strong></h2> </div> <div> <p><strong>Fig4a. Annual maximum charging power within each hexagonal grid across Beijing, 4c Distributions of the three clusters of temporal charging profiles in Beijing, and 4d Share of clusters by city. </strong></p> </div> <div> <p><strong>FigS7-FigS12. Spatial distributions of charging power (kW): Max charging power and cluster distributions (City name). </strong></p> </div> <div> <p>File: max_power_cluster_cities.shp </p> </div> <div> <p>Description: This dataset covers the maximum charging power distribution across seven Chinese cities, using H3 grids with Resolution 8 (~0.74 km²). </p> </div> <div> <p>Cluster 0, 1, and 2 are defined based on the temporal profiles of charging power in the grids. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city </p> </div> </div> </td> <td> <div> <div> <p>Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, Chengdu, and Chongqing </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>hex_id </p> </div> </div> </td> <td> <div> <div> <p>Hexagon ID of H3 system with Resolution 8. </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>cluster_id </p> </div> </div> </td> <td> <div> <div> <p>This indicates the cluster index of each hexagon. </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>max_power </p> </div> </div> </td> <td> <div> <div> <p>Maximum charging power. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>geometry </p> </div> </div> </td> <td> <div> <div> <p>Hexagons in EPSG: 4326 – WGS 84. </p> </div> </div> </td> <td> <div> <div> <p>Polygon </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>8. Temporal Patterns of Charging Power </strong></h2> </div> <div> <p><strong>Fig 4b Three unique clusters of daily temporal patterns of charging power (all cities) </strong></p> </div> <div> <p>File: clusters_tempo.csv </p> </div> <div> <p>Description: Temporal variations of charging power aggregated from all hexagons in each cluster. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>cluster_id </p> </div> </div> </td> <td> <div> <div> <p>This indicates the cluster index of each hexagon. </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>t </p> </div> </div> </td> <td> <div> <div> <p>Hourly index (0-23) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>q25 </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of charging power. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>q50 </p> </div> </div> </td> <td> <div> <div> <p>The median value of charging power. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>q75 </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of charging power. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Type </p> </div> </div> </td> <td> <div> <div> <p>Weekday/Weekend. </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h1><strong>Supplementary Information: </strong></h1> </div> <div> <h2><strong>S1. Accuracy and Quality of Data Collection: GPS Measurement Accuracy </strong></h2> </div> <div> <p><strong>FigSI1.Histogram of spatial errors in GPS Measurements </strong></p> </div> <div> <p>File: FigSI1.Histogram of spatial errors in GPS Measurements.csv </p> </div> <div> <p>Description: Analysis of the accuracy of GPS data used in the study. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval </p> </div> </div> </td> <td> <div> <div> <p>The interval of spatial error </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>m </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height </p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h2><strong>S2. Charging Behavior Analysis </strong></h2> </div> <div> <h3><strong>Empirical Distributions of Charger Power Delivered: </strong></h3> </div> <div> <p><strong>FigSI2-1.Distributions of charger power delivered to cars </strong></p> </div> <div> <p>File: FigSI2-1.Empirical distributions of charger power delivered to cars.csv </p> </div> <div> <p>Description: Analysis of the distribution of charger power for passenger cars. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval </p> </div> </div> </td> <td> <div> <div> <p>The interval of charging power </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height </p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>FigSI2-2.Empirical distributions of charger power delivered to buses </strong></p> </div> <div> <p>File: FigSI2-2.Empirical distributions of charger power delivered to buses.csv </p> </div> <div> <p>Description: Analysis of the distribution of charger power for buses. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval </p> </div> </div> </td> <td> <div> <div> <p>The interval of charging power </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height </p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <p><strong>FigSI2-3.Empirical distributions of charger power delivered to SPVs </strong></p> </div> <div> <p>File: FigSI2-3.Empirical distributions of charger power delivered to SPVs.csv </p> </div> <div> <p>Description: Analysis of the distribution of charger power for special purpose vehicles (SPVs). </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval </p> </div> </div> </td> <td> <div> <div> <p>The interval of charging power </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>kW </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height </p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h3><strong>Charging Power Preferences: </strong></h3> </div> <div> <p><strong>FigSI3.Distribution of charging power level preferences among different EV types </strong></p> </div> <div> <p>File: FigSI3.Distribution of charging power level preferences among different EV types.csv </p> </div> <div> <p>Description: Analysis of charging power level preferences for different EV types. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 & P2 & P3 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 & P2 & P3 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P2 & P3 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P2 & P3 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 & P3 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 & P3 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 & P2 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 & P2 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P3 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P3 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P2 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P2 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 </p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 chargers to the total number of that EV type </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>% </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h3><strong>Charging Event Durations </strong></h3> </div> <div> <p><strong>FigSI4.Average duration (hr) of charging events by type of charging energy for different vehicle types </strong></p> </div> <div> <p>File: Average duration (hr) of charging events by type of charging energy for different vehicle types.csv </p> </div> <div> <p>Description: Analysis of the average duration of charging events categorized by energy type. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>vehicle type_charging duration_P </p> </div> </div> </td> <td> <div> <div> <p>Take Private car_charging duration_P1 as an example, it refers to charging duration of private cars charging with P1 </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The charging duration corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of charging duration. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of charging duration. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of charging duration. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The charging duration corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h3><strong>Vehicle Usage Patterns and Energy Metrics </strong></h3> </div> <div> <p><strong>FigSI5.Distributions of average daily driving distance by vehicle type </strong></p> </div> <div> <p>File: FigSI5.Distributions of average daily driving distance by vehicle type.csv </p> </div> <div> <p>Description: Distribution analysis of daily driving distances across different vehicle types and cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_vehicle type </p> </div> </div> </td> <td> <div> <div> <p>Take Beijing_Private car as an example, it refers to average daily driving distance of private cars in Beijing </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The average daily driving distance corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of average daily driving distance. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of average daily driving distance. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of average daily driving distance. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The average daily driving distance corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div> <div> <h3><strong>Battery Energy Distribution: </strong></h3> </div> <div> <p><strong>FigSI6.Distributions of nominal battery energy by vehicle type </strong></p> </div> <div> <p>File: FigSI6.Distributions of nominal battery energy by vehicle type.csv </p> </div> <div> <p>Description: Analysis of nominal battery energy distributions across vehicle types and cities. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_vehicle type </p> </div> </div> </td> <td> <div> <div> <p>Take Beijing_Private car as an example, it refers to nominal battery energy of private cars in Beijing </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker </p> </div> </div> </td> <td> <div> <div> <p>The nominal battery energy corresponding to the Lower Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%) </p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of nominal battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%) </p> </div> </div> </td> <td> <div> <div> <p>The median value of nominal battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%) </p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of nominal battery energy. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker </p> </div> </div> </td> <td> <div> <div> <p>The nominal battery energy corresponding to the Upper Whisker of the box plot. </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p> </p> </div>
Data on the Swiss energy system and electric vehicles
<p>This repository gathers the data used in the paper:</p> <p>Loris Di Natale, Luca Funk, Martin Rüdisüli, Bratislav Svetozarevic, Giacomo Pareschi, Philipp Heer and Giovanni Sansavini. <strong>The Potential of Vehicle-to-Grid to Support the Energy Transition: A Case Study on Switzerland. </strong><em>Energies.</em> 2021; 14(16):4812. <a href="https://doi.org/10.3390/en14164812">https://doi.org/10.3390/en14164812</a>.</p> <p>The linked code can be found <a href="https://gitlab.nccr-automation.ch/loris.dinatale/v2g-in-switzerland">here</a>.</p> <p>Small description of the different files:</p> <ul> <li><em>Car_trips.csv:</em> List of trips from different cars in Switzerland.<br> Data provided by Giacomo Pareschi and based on the result of the 2015 edition of MZMV (Bundesamt für Statistik / Bundesamt für Raumentwicklung, Verkehrsverhalten der Bevölkerung, Ergebnisse des Mikrozensus Mobilität und Verkehr 2015, Neuchâtel und Bern (2017), <a href="https://www.are.admin.ch/are/de/home/mobilitaet/grundlagen-und-daten/mzmv.html">https://www.are.admin.ch/are/de/home/mobilitaet/grundlagen-und-daten/mzmv.html</a> ). Each weekly profile is not representative and any result obtained with less than 50 profiles should be interpreted with extreme caution.</li> <li><em>ch.bfe.ladestellen-elektromobilitaet.json:</em> Data on the charging stations in Switzerland.<br> Online data from the Swiss Federal Office of Energy.</li> <li><em>cs_power_Home.csv</em> and<em> cs_power_Work.csv: </em>Own data on the charging powers of charging stations located at home or at work.</li> <li><em>energy_system_model_empa_results_sc_1.csv: </em>Swiss Energy System model used in our work to generate the fixed hydropower output profile.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>EVs_cap.csv:</em> Data on different EV brands, from own research.</li> <li><em>gCO2_eq_kWh_techs.csv: </em>CO2-equivalent greenhouse gas emission factors for different technologies, from own research.</li> <li><em>Heat_BEV_demand_2018.csv:</em> Electricity demand for heating and EVs in Switzerland.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>inflows_Beer.csv: </em>Data on the water inflows in the Swiss dams over the year.<br> Data provided by Michael Beer, from Beer, M. Abschätzung des Potenzials der Schweizer Speicherseen zur Lastdeckung bei Importrestriktionen. Z. Energiewirtschaft <strong>2018</strong>, 42, 1–12.</li> <li><em>MeteoSchweiz_pop_weight_2018.csv:</em> Temperature data in Switzerland taken from MeteoSwiss and population-weighted.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>Scenarios.csv </em>and <em>Scenarios+.csv: </em>Different scenarios for electricity production and consumption, generated in-house based on <ul> <li>the Energy Strategy 2050 (Kirchner, A.; Bredow, D.; Ess, F.; Grebel, T.; Hofer, P.; Kemmler, A.; Ley, A.; Piégsa, A.; Schütz, N.; Strassburg, S.; et al. Energy Perspectives, Die Energieperspektiven für die Schweiz bis 2050; Prognos AG: Basel, Switzerland, 2012), respectively</li> <li>the Energy Strategy 2050+ (Prognos AG and INFRAS AG and TEP Energy GmbH and Ecoplan AG. ENERGIEPERSPEKTIVEN 2050+ Kurzbericht. 2020).</li> </ul> </li> <li><em>transfer_15min_2018.csv: </em>Swiss power system model of electricity production and consumption in 2018.<br> Data provided by Dr. Martin Rüdisüli.</li> </ul> <p> </p>
Testing 3D modelling software. Modelling charging pads for WPT of electric vehicles for EM emissions simulation.
<p>Even for the experienced 3D FEM modelers it may not be obvious which geometry discretization is the most appropriate and suitable for this type of problem. It may be a conservative approach to test the computation tool on a simplified geometry, on which the magnetic field distribution is known. As part of the “Metrology for inductive charging of electric vehicles” (MICEV) project (www.micev.eu), an axisymmetric geometry was used, with the results reported.</p>
California Electric Vehicle Loads by Feeder Circuit
Open the record for dataset details and reuse information.
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Supporting data to the paper "Modelling charge profiles of electric vehicles based on charges data"
<p>This dataset contains the<em> underling data</em> and the <em>extended data</em> for the paper Modelling charge profiles of electric vehicles based on charges data”, submitted for consideration and open review in Open Research Europe.</p> <p>In the follow the description of the files is reported:</p> <p>HISTORIC DATA 2019 ELECTROLINERES AMB.csv: contains information on the charge events at the public charging points managed by the municipality in the metropolitan area of Barcelona in 2019. Fields are: charging point name; connector typology and number; charge start time; charge stop time; charge duration in minutes, energy delivered in kWh; vehicle manufacturer (optional); vehicle model (optional).</p> <p>STATIC INFORMATION CHARGING POINTS AMB 29042020.csv: contains the information about the public charging points of the metropolitan area of Barcelona. Fields are: charger typology (Quick/Normal); Charging point name and address; OCCP version; charger location; longitude; latitude; 7 flag fields for the connector type; observations; charging point maker.</p> <p>Lataustapahtumat, julkiset latauslaitteet 2019.csv: contains the information about the Turku Energia charge events for the city of Turku in 2019. Fields are: date of record creation, Station ID, Station name, charge start time, charge stop time, charge duration in minutes, energy delivered in Wh, Plug type (AC 22 kW/DC 50 kW), Cumulative energy delivered in the year (Wh), Average charge power (W)</p> <p>EV.csv: containes data on battery size retrived from vehicle datasheet or manufacturer website. Fields are: record ID, vehicle manufacturer ; vehicle model; battery size in kWh.</p> <p>Charge2019_EV_AMB.csv: contains the data on charge requests ( HISTORIC DATA 2019 ELECTROLINERES AMB.csv ) combined with the information on vehicle battery (EV.csv).</p> <p> </p>
References and Metadata for Electric vehicles' consumer behaviours: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011)
<p>The bibliography and metadata used for the analysis published in the Journal of Business Research - Electric vehicles' consumer behaviors: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011).</p>
Synthetic Dataset of charging processes by electric vehicles at workplace in Germany
<p>The dataset shows eight cluster groups that depict the mobility behavior of electric vehicle users in the employee context. For this purpose, 23.9 million data entries were analyzed, corresponding to 37,238 charging sessions. These data were collected over the year 2023. The 220 charging points were exclusively accessible to employees (private use case). From the data, cluster groups were derived using the Gaussian Mixture Model, and a synthetic dataset was generated through Monte Carlo sampling.</p> <p><span>The dataset consists of 8000 synthetic profiles, offering a robust scientific basis. By retaining the same statistical attributes as the empirical data, the synthetic profiles represent eight different mobility clusters, each containing 1000 entries, including full-time and part-time employees, shift workers, pool vehicle users, and opportunists.</span> Each cluster is represented by the mean parking start hours (arrival time - in decimal hours), mean parking duration (in decimal hours), the average energy recharged, and the average charging duration, each including the cluster-specific standard deviation and median.</p> <p>Further information can be obtained from the upcoming publication: "Synthetic Dataset of charging processes by electric vehicles at workplace in Germany."</p>
Number of BEV (a battery electric vehicle) and PHEV (a plug-in hybrid electric vehicle) vehicles for each Country (2019)
<p>According to the Global E.V. Outlook 2020, China ranks first in vehicles in operation with electric or hybrid engines. In second place in the U.S. and third place in Norway.</p>
Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems
<p>This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.</p>
Dataset: "On the influence of AVAS directivity on electric vehicle speed perception"
<p>This repository contains experiment results and calibrated stimuli recordings accompanying the publication: </p> <blockquote> <p>Leon Müller and Wolfgang Kropp, <em>On the influence of AVAS directivity on electric vehicle speed</em><br><em>perception</em>, submitted for publication in Inter-Noise 2024 proceedings</p> </blockquote> <p>The stimuli recordings were obtained by placing a calibrated artificial head (<em>HEAD Acoustics HMS-V</em>) at the participant listening position in the anechoic chamber.</p> <p>The AVAS sounds were generated using the Electric Vehicle Auralization Toolbox presented in:</p> <blockquote> <p>Müller L. & Kropp W. 2024. Auralization of electric vehicles for the perceptual evaluation of acoustic vehicle alerting systems. Acta Acustica, 8, 27. https://doi.org/10.1051/aacus/2024025</p> </blockquote> <p>The .wav files contain 32-bit float values that correspond to pressure in Pa and are named according to the following table.</p> <table> <tbody> <tr> <td><strong>AVAS Signal</strong></td> <td><strong>Directivity</strong></td> <td><strong>Vehicle Speed</strong></td> </tr> <tr> <td>T: Tonal (VW ID.3)</td> <td>B: BEM</td> <td>10: 10 km/h</td> </tr> <tr> <td>N: Noise (Tesla Model Y)</td> <td>C: Cardioid</td> <td>20: 20 km/h</td> </tr> <tr> <td> </td> <td>S: Star</td> <td> </td> </tr> <tr> <td> </td> <td>O: Omnidirectional</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Data for "Do electric vehicles mitigate urban heat? The case of a tropical city"
<p>This dataset contains the underlying data used in the publication "Do electric vehicles mitigate urban heat? The case of a tropical city", which is under review in <em>Front. Environ. Sci. .</em></p> <p>The dataset includes two folders:</p> <p>1. <strong>data</strong> <br> Include COSMO-DCEP-BEP model inputs and output needed to reproduce the results in the manuscript (NetCDF). </p> <p>2. <strong>script</strong><br> Include post-processing scripts used to generate the figures in the manuscript (Jupiter Python 3 Notebook).</p> <p><em> </em></p>
Sound modelling techniques for an interactive audio-rendering simulation of an electric vehicle
<p>Dataset for conference paper "Sound modelling techniques for an interactive audio-rendering simulation of an electric vehicle"</p>
Dataset: Invesco Electric Vehicle Metals Commodity Strategy No K-1 ETF (EVMT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Global X Autonomous & Electric Vehicles ETF (DRIV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Zapp Electric Vehicles Group Limited (ZAPPW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Zapp Electric Vehicles Group Limited (ZAPP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.