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38 results for “electrical charge”

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zenodo52/100

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 ie&sup3; 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>ie&sup3; 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>

opencc-by-4.0Dec 2023View details →
zenodo48/100

Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach

<p>Contact details:</p> <p>wasim.shoman at chalmers.se&nbsp;</p> <p>waahh7 at gmail com</p> <p><strong>Abstract of the research:</strong></p> <p>Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO<sub>2</sub> emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions. However, they require sufficient charging infrastructure to support long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operation (travel times over 4.5 hours or over 360 km distance traveled) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25&acute;25 km<sup>2</sup>&nbsp;square with each square that could&nbsp;include multiple charging stations and parking lots of multiple charging points) needs to have four to five times more overnight than megawatt charging points. We estimate that about 40,000 overnight charging points (50-100 kW, combined charging system, CCS) and about 9,000 megawatt charging system (MCS, 0.7 &ndash; 1.2 MW) points are required for 15% of trucks as BETs in long-haul operation. On average, 8 and 2 CCS and MCS chargers are required per charging area, and each MCS and CCS serve, on average, 11 and 2 BETs daily, respectively. Public charging entails about 110 GWh daily electricity demand in each charging area. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization.</p> <p><strong>The methodology:</strong></p> <p>We develop a method to place charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by HDV is identified using the ETISplus dataset.&nbsp;We develop a travel pattern for the HDV&nbsp;to convert&nbsp;flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving HDV. Break and rest locations for all moving HDVs are aggregated to suggest energy requirements if assuming these HDVs are BETs. The aggregated energy to charge stopped BETs is used to identify the number and type of chargers within each suggested charging station.</p> <p><strong>Datasets details</strong></p> <p>The presented&nbsp;datasets contain&nbsp;spatial information for generating charger stations with specifications according to charging needs. The datasets contain&nbsp;information about:&nbsp;Transport network model and edges,&nbsp;Transported flows, routes and flow center information&nbsp;data, region centers, and Planned transport infrastructure.&nbsp;</p> <p>The first dataset titled &#39;ChargerLocations&#39; contains information about the locations of suggested charging stations, the number and type of chargers, and the number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields:</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> <td>Data Type</td> <td>Unit</td> </tr> <tr> <td>DTN30/MainDTN</td> <td>&nbsp;number of electrified trucks in 2030</td> <td>integer&nbsp;</td> <td>number</td> </tr> <tr> <td>ChE30</td> <td>&nbsp;charged energy in Mega watt-hour from all charging (fast and slow)</td> <td>float</td> <td>&nbsp;Mega watt-hour</td> </tr> <tr> <td>ChERM</td> <td>&nbsp;charged energy in Megawatt hour with slow charging only (rest)</td> <td>float</td> <td>&nbsp;Mega watt-hour</td> </tr> <tr> <td>MDTN_R</td> <td>&nbsp;number of electrified trucks using slow chargers (rest)</td> <td>integer&nbsp;</td> <td>number</td> </tr> <tr> <td>ChEBM</td> <td>&nbsp;charged energy in Megawatt hour with fast charging only (break)</td> <td>float</td> <td>&nbsp;Mega watt-hour</td> </tr> <tr> <td>MDTN_B</td> <td>&nbsp;number of electrified trucks using fast chargers (break)</td> <td>integer&nbsp;</td> <td>number</td> </tr> <tr> <td>NSCh2pD</td> <td>&nbsp;number of slow chargers</td> <td>integer&nbsp;</td> <td>number</td> </tr> <tr> <td>NFCh30m</td> <td>&nbsp;number of fast chargers</td> <td>integer&nbsp;</td> <td>number</td> </tr> <tr> <td>TotCha</td> <td>&nbsp;Total number of chargers</td> <td>integer&nbsp;</td> <td>number</td> </tr> </tbody> </table> <p>&nbsp;</p> <p>The second dataset titled (RestandBreaksPoints.shp) with information about the rest and break point locations. The dataset includes detailes about stop type, number of stopped trucks, and required charged energy. The dataset is a shapefile with &quot;shp&quot; format.&nbsp;</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data Type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>ID_origin_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_origin_region</p> </td> <td> <p>National name of NUTS-3 region of origin</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ID_destination_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_destination_<br> region</p> </td> <td> <p>National name of NUTS-3 destination region</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Rest</p> </td> <td> <p>A value of &rdquo;1&rdquo; indicates a rest stop</p> </td> <td> <p>Boolean</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Break</p> </td> <td> <p>A value of &rdquo;1&rdquo; indicates a break stop</p> </td> <td> <p>Boolean</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ChaDisKM</p> </td> <td> <p>Charged range within a trip for stopped the truck</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>ChaEnekWh</p> </td> <td> <p>Charged energy within a trip for stopped the truck</p> </td> <td> <p>Float</p> </td> <td> <p>KWh</p> </td> </tr> <tr> <td> <p>MainDTN</p> </td> <td> <p>Number of stopped trucks for the main electrification scenario (15%)</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>ChE30M</p> </td> <td> <p>Charged energy for all stopped trucks</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>ChERM</p> </td> <td> <p>Charged energy for the trucks stopping for rest</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>MDTN_R</p> </td> <td> <p>Number of trucks stopping for rest</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>ChEBM</p> </td> <td> <p>Charged energy for the trucks stopping for break</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>MDTN_B</p> </td> <td> <p>Number of trucks stopping for break</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>X, Y coordinates</p> </td> <td> <p>geometry</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>&nbsp;</p> <p>&nbsp;</p> <p>The following dataset titled &#39;flowFile&#39; with information about the transported flow between regions and the transported routes. The dataset is in &quot;CSV&quot; format.&nbsp;Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X):</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>ID_origin_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_origin_region</p> </td> <td> <p>National name of NUTS-3 region of origin</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ID_destination_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_destination_<br> region</p> </td> <td> <p>National name of NUTS-3 destination region</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Edge_path_E_road</p> </td> <td> <p>List of the&nbsp;<em>network edge IDs</em>&nbsp;of the shortest path between the O-D pair, determined with Dijkstra&#39;s algorithm</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Distance_from_origin_<br> region_to_E_road</p> </td> <td> <p>Distance from the geometric centre of the origin region to the closest network node</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Distance_within_E_<br> road</p> </td> <td> <p>Distance of the shortest edge path between the O-D pair</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Distance_from_E_<br> road_to_destination_<br> region</p> </td> <td> <p>Distance from the geometric centre of the destination region to the closest network node</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Total_distance</p> </td> <td> <p>Sum of&nbsp;<em>Distance_from_origin_region_to_E_road, Distance_within_E_road</em>&nbsp;and&nbsp;<em>Distance_from_E_road_to_destination_region</em></p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2010</p> </td> <td> <p>Number of trucks that drive between the O-D pair in 2010</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2019</p> </td> <td> <p>Number of trucks that drive between the O-D pair after they had been scaled to 2019</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2030</p> </td> <td> <p>Number of trucks that drive between the O-D pair according to the forecast for 2030</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2010</p> </td> <td> <p>Number of tons that are transported between the O-D pair in 2010 according to ETISplus</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2019</p> </td> <td> <p>Number of tons that are transported between the O-D pair after they had been scaled to 2019</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2030</p> </td> <td> <p>Number of tons that are transported between the O-D pair according to the forecast for 2030</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> </tbody> </table> <p>Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in &quot;CSV&quot; format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Network_Node_ID</p> </td> <td> <p>Unique network node ID</p> </td> <td> <p>Integer (6 digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Network_Node_X</p> </td> <td> <p>Longitude of the location of network node</p> </td> <td> <p>Float</p> </td> <td> <p>Degrees</p> </td> </tr> <tr> <td> <p>Network_Node_Y</p> </td> <td> <p>Latitude of the location of network node</p> </td> <td> <p>Float</p> </td> <td> <p>Degrees</p> </td> </tr> <tr> <td> <p>ETISplus_Zone_ID</p> </td> <td> <p>ID of the NUTS-3 region in which the network node is located</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Country</p> </td> <td> <p>Unique country code of the country in which the network node is located (country codes are defined by ETISplus)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>Description of variables used in the network edges list (Updated_04_network-edges). The dataset is in &quot;CSV&quot; format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Network_Edge_ID</p> </td> <td> <p>Unique edge ID</p> </td> <td> <p>Integer<br> (7 digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Manually_Added</p> </td> <td> <p>Determines whether an edge had been manually added to the network (1) or not (0)</p> </td> <td> <p>Binary-integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Distance</p> </td> <td> <p>Length of the network edge</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Network_Node_A_ID</p> </td> <td> <p>Unique ID of the network node that defines one end point of the network edge</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Network_Node_B_ID</p> </td> <td> <p>Unique ID of the network node that defines one end point of the network edge</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_2019</p> </td> <td> <p>Number of trucks that drive on the edge in 2019 (both highway directions combined)</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_2030</p> </td> <td> <p>Number of trucks that drive on the edge in 2030 (both highway directions combined)</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> </tbody> </table> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Oct 2022View details →
zenodo48/100

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>

opencc-by-4.0Jul 2024View details →
zenodo48/100

Electric Vehicle Usage and Charging Analysis Dataset Across Seven Major Cities in China

<div> <h1><strong>Background&nbsp;</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&mdash;including private cars, taxis, buses, and special purpose vehicles (SPVs).&nbsp;&nbsp;&nbsp;</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.&nbsp;</p> </div> <div> <p>&nbsp;</p> <h1><strong>Data description&nbsp;</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.&nbsp;</p> </div> <div> <p>&nbsp;</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&nbsp;</strong></p> </div> <div> <p>File: Fig1a.Distribution of EV types across selected Chinese cities.csv&nbsp;</p> </div> <div> <p>Description: Distribution of EV types across seven cities, detailing the share of different vehicle types.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Beijing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Beijing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shenzhen&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Shenzhen&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shanghai&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Shanghai&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Guangzhou&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Guangzhou&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chengdu&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Chengdu&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chongqing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Chongqing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Nanjing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Distribution of EV types in Nanjing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <p><strong>Fig1b.Distribution of battery energy by vehicle types&nbsp;</strong></p> </div> <div> <p>File: Fig1b.Distribution of battery energy by vehicle types.csv&nbsp;</p> </div> <div> <p>Description: Distribution of battery energy across different vehicle types, represented as box plot statistics.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type_2&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>vehicle types&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The battery energy corresponding to the Lower Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of battery energy.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The median value of battery energy.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of battery energy.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The battery energy corresponding to the Upper Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <h2><strong>2. Variations in Battery Energy</strong></h2> </div> <div> <p><strong>Fig1c.Variations of battery energy of buses &nbsp;</strong></p> </div> <div> <p>File: Fig1c.Variations of battery energy of buses across studied cities.csv&nbsp;</p> </div> <div> <p>Description: Battery energy variations for buses across the studied cities.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_En&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>English name of 7 Chinese city&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The battery energy of buses corresponding to the Lower Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of battery energy of buses.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The median value of battery energy of buses.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of battery energy of buses.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The battery energy of buses corresponding to the Upper Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <p><strong>Fig1d.Variations of battery energy of SPVs &nbsp;</strong></p> </div> <div> <p>File: Fig1c.Variations of battery energy of SPVs across studied cities.csv&nbsp;</p> </div> <div> <p>Description: Battery energy variations for special purpose vehicles (SPVs) across cities.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_En&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>English name of 7 Chinese city&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The battery energy of SPVs corresponding to the Lower Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of battery energy of SPVs.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The median value of battery energy of SPVs.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of battery energy of SPVs.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The battery energy of SPVs corresponding to the Upper Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</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&nbsp;</strong></p> </div> <div> <p>File: Fig1e.Daily driving distance of different vehicle types.csv&nbsp;</p> </div> <div> <p>Description: Cumulative distribution functions (CDFs) of daily driving distances for various vehicle types.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>CDF Percentile&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>CDF Percentile&nbsp;</p> </div> <div> <p>&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Integer&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Private car&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The value of private car daily driving distance corresponding to CDF Percentile&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Official car&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The value of official car daily driving distance corresponding to CDF Percentile&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>SPV&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The value of SPV daily driving distance corresponding to CDF Percentile&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Rental car&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The value of rental car daily driving distance corresponding to CDF Percentile&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Bus&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The value of bus daily driving distance corresponding to CDF Percentile&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Taxi&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The value of taxi daily driving distance corresponding to CDF Percentile&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>km&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <p><strong>Fig1f-1. Ratio of daily energy consumed over battery energy&nbsp;</strong></p> </div> <div> <p>File: Fig1f-1.The ratio of daily energy consumed over battery energy.csv&nbsp;</p> </div> <div> <p>Description: Ratio of daily energy consumption relative to battery energy for each vehicle type.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type_2&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>vehicle types&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The energy ratio corresponding to the Lower Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of energy ratio.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The median value of energy ratio.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of energy ratio.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The energy ratio corresponding to the Upper Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</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&nbsp;</p> </div> <div> <p>Description: Data on the number of daily charging events across vehicle types.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type_2&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>vehicle types&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The charging events per day corresponding to the Lower Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of charging events per day.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The median value of charging events per day.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of charging events per day.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The charging events per day corresponding to the Upper Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</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 &nbsp;</strong></p> </div> <div> <p>File: Fig2a.Daily usage patterns of EVs across different vehicle types and days.csv&nbsp;</p> </div> <div> <p>Description: Usage patterns of EVs by type and day, segmented into 15-minute intervals.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Vehicle type_day type_state&nbsp;</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&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <p><strong>Fig2b. SOC levels before and after charging&nbsp;</strong></p> </div> <div> <p>File: Fig2b. SOC levels before and after charging by charging level by vehicle type.csv&nbsp;</p> </div> <div> <p>Description: SOC levels before and after charging events, classified by charging level and vehicle type.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>vehicle_SOC_P&nbsp;</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&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The SOC corresponding to the Lower Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of SOC.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The median value of SOC.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of SOC.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The SOC corresponding to the Upper Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</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&nbsp;</strong></p> </div> <div> <p>File: Fig2c-top.Energy consumption rate (ECR) of passenger cars by month of the year.csv&nbsp;</p> </div> <div> <p>Description: Monthly ECR of passenger cars in different cities.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Beijing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Beijing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh/100km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shenzhen&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Shenzhen&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh/100km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Shanghai&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Shanghai&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh/100km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Guangzhou&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Guangzhou&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh/100km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chengdu&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Chengdu&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh/100km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Chongqing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Chongqing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh/100km&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Nanjing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>ECR of passenger cars by month in Nanjing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh/100km&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <p><strong>Fig2c-bottom.ECR of passenger cars as a function of temperature&nbsp;</strong></p> </div> <div> <p>File: Fig2c-bottom.ECR of passenger cars as a function of temperature.csv&nbsp;</p> </div> <div> <p>Description: Passenger vehicle ECR in relation to temperature across different cities.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Temperature&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Temperature of a city in a certain month&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>℃&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>ECR&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Average energy consumption rate of passenger cars of a city in a certain month&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kWh/100km&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</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&nbsp;</strong></p> </div> <div> <p>File: Fig3-1.Number of vehicles being charged by level by time of day.csv&nbsp;</p> </div> <div> <p>Description: Number of vehicles charging at different power levels throughout the day.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Vehicle type_P_day type&nbsp;</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&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Integer&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <p><strong>Fig3-2.Daily charging load from electric vehicles &nbsp;</strong></p> </div> <div> <p>File: Fig3-2.Daily charging load from electric vehicles across different vehicle types and power level.csv&nbsp;</p> </div> <div> <p>Description: Charging load data across vehicle types and power levels, aggregated by time of day.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Vehicle type_P_day type&nbsp;</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&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <h2><strong>7. Spatial Distribution of Max Charging Power&nbsp;</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.&nbsp;</strong></p> </div> <div> <p><strong>FigS7-FigS12. Spatial distributions of charging power (kW): Max charging power and cluster distributions (City name).&nbsp;</strong></p> </div> <div> <p>File: max_power_cluster_cities.shp&nbsp;</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 km&sup2;).&nbsp;</p> </div> <div> <p>Cluster 0, 1, and 2 are defined based on the temporal profiles of charging power in the grids.&nbsp;&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, Chengdu, and Chongqing&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>hex_id&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Hexagon ID of H3 system with Resolution 8.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>cluster_id&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>This indicates the cluster index of each hexagon.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Integer&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>max_power &nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Maximum charging power.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kW&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>geometry&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Hexagons in EPSG: 4326 &ndash; WGS 84.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Polygon&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <h2><strong>8. Temporal Patterns of Charging Power&nbsp;</strong></h2> </div> <div> <p><strong>Fig 4b Three unique clusters of daily temporal patterns of charging power (all cities)&nbsp;</strong></p> </div> <div> <p>File: clusters_tempo.csv&nbsp;</p> </div> <div> <p>Description: Temporal variations of charging power aggregated from all hexagons in each cluster.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>cluster_id&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>This indicates the cluster index of each hexagon.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Integer&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>t&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Hourly index (0-23)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Integer&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>q25&nbsp;&nbsp;&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of charging power.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kW&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>q50&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The median value of charging power.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kW&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>q75&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of charging power.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kW&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Weekday/Weekend.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <h1><strong>Supplementary Information:&nbsp;</strong></h1> </div> <div> <h2><strong>S1. Accuracy and Quality of Data Collection: GPS Measurement Accuracy&nbsp;</strong></h2> </div> <div> <p><strong>FigSI1.Histogram of spatial errors in GPS Measurements&nbsp;</strong></p> </div> <div> <p>File: FigSI1.Histogram of spatial errors in GPS Measurements.csv&nbsp;</p> </div> <div> <p>Description: Analysis of the accuracy of GPS data used in the study.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The interval of spatial error&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>m&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <h2><strong>S2. Charging Behavior Analysis&nbsp;</strong></h2> </div> <div> <h3><strong>Empirical Distributions of Charger Power Delivered:&nbsp;</strong></h3> </div> <div> <p><strong>FigSI2-1.Distributions of charger power delivered to cars&nbsp;</strong></p> </div> <div> <p>File: FigSI2-1.Empirical distributions of charger power delivered to cars.csv&nbsp;</p> </div> <div> <p>Description: Analysis of the distribution of charger power for passenger cars.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The interval of charging power&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kW&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <p><strong>FigSI2-2.Empirical distributions of charger power delivered to buses&nbsp;</strong></p> </div> <div> <p>File: FigSI2-2.Empirical distributions of charger power delivered to buses.csv&nbsp;</p> </div> <div> <p>Description: Analysis of the distribution of charger power for buses.&nbsp;&nbsp;&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The interval of charging power&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kW&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <p><strong>FigSI2-3.Empirical distributions of charger power delivered to SPVs&nbsp;</strong></p> </div> <div> <p>File: FigSI2-3.Empirical distributions of charger power delivered to SPVs.csv&nbsp;</p> </div> <div> <p>Description: Analysis of the distribution of charger power for special purpose vehicles (SPVs).&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Interval&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The interval of charging power&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>kW&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Height&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The height of each column in the histogram&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <h3><strong>Charging Power Preferences:&nbsp;</strong></h3> </div> <div> <p><strong>FigSI3.Distribution of charging power level preferences among different EV types&nbsp;</strong></p> </div> <div> <p>File: FigSI3.Distribution of charging power level preferences among different EV types.csv&nbsp;</p> </div> <div> <p>Description: Analysis of charging power level preferences for different EV types.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 &amp; P2 &amp; P3&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 &amp; P2 &amp; P3 chargers to the total number of that EV type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P2 &amp; P3&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P2 &amp; P3 chargers to the total number of that EV type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 &amp; P3&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 &amp; P3 chargers to the total number of that EV type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1 &amp; P2&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The ratio of each EV type's number of P1 &amp; P2 chargers to the total number of that EV type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P3&nbsp;</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&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P2&nbsp;</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&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>P1&nbsp;</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&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>%&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <h3><strong>Charging Event Durations&nbsp;</strong></h3> </div> <div> <p><strong>FigSI4.Average duration (hr) of charging events by type of charging energy for different vehicle types&nbsp;</strong></p> </div> <div> <p>File: Average duration (hr) of charging events by type of charging energy for different vehicle types.csv&nbsp;</p> </div> <div> <p>Description: Analysis of the average duration of charging events categorized by energy type.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>vehicle type_charging duration_P&nbsp;</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&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The charging duration corresponding to the Lower Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of charging duration.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The median value of charging duration.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of charging duration.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The charging duration corresponding to the Upper Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <h3><strong>Vehicle Usage Patterns and Energy Metrics&nbsp;</strong></h3> </div> <div> <p><strong>FigSI5.Distributions of average daily driving distance by vehicle type&nbsp;</strong></p> </div> <div> <p>File: FigSI5.Distributions of average daily driving distance by vehicle type.csv&nbsp;</p> </div> <div> <p>Description: Distribution analysis of daily driving distances across different vehicle types and cities.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_vehicle type&nbsp;</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&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The average daily driving distance corresponding to the Lower Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of average daily driving distance.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The median value of average daily driving distance.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of average daily driving distance.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The average daily driving distance corresponding to the Upper Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div> <div> <h3><strong>Battery Energy Distribution:&nbsp;</strong></h3> </div> <div> <p><strong>FigSI6.Distributions of nominal battery energy by vehicle type&nbsp;</strong></p> </div> <div> <p>File: FigSI6.Distributions of nominal battery energy by vehicle type.csv&nbsp;</p> </div> <div> <p>Description: Analysis of nominal battery energy distributions across vehicle types and cities.&nbsp;</p> </div> <div> <div> <div>&nbsp;</div> <table> <tbody> <tr> <td> <div> <div> <p>Column&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Description&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Data type&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Unit&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>city_vehicle type&nbsp;</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&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>String&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Lower Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The nominal battery energy corresponding to the Lower Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q1 (25%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 25th percentile value of nominal battery energy.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Median (50%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The median value of nominal battery energy.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Q3 (75%)&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The 75th percentile value of nominal battery energy.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>Upper Whisker&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>The nominal battery energy corresponding to the Upper Whisker of the box plot.&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>Float&nbsp;</p> </div> </div> </td> <td> <div> <div> <p>-&nbsp;</p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <p>&nbsp;</p> </div>

opencc-by-4.0Sep 2024View details →
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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 &ldquo;Metrology for inductive charging of electric vehicles&rdquo; (MICEV) project (www.micev.eu), an axisymmetric geometry was used, with the results reported.</p>

opencc-by-4.0Jan 2021View details →
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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&nbsp;Modelling charge profiles of electric vehicles based on charges data&rdquo;, submitted&nbsp;&nbsp;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>&nbsp;</p>

opencc-by-4.0Nov 2021View details →
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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>

opencc-by-4.0Jul 2024View details →
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Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes

<p><strong>Background</strong></p> <p>Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the &quot;Liquid-fuel&quot; mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation&#39;s input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc.</p> <p>A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation).</p> <p><a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a></p> <p><strong>Methodology</strong></p> <p>This data product is centered on the 1.7 million inhabitants of the V&auml;stra G&ouml;taland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden.</p> <p>This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents&#39; activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging &amp; discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7).</p> <p><strong>Data description</strong></p> <p>The current data product covers seven data files.</p> <p><strong>(1) Agents&#39; experienced activity plans</strong></p> <p>File name: 1_activity_plans.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>act_id</p> </td> <td> <p>Activity index of each agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>deso</p> </td> <td> <p>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>POINT_X</p> </td> <td> <p>Coordinate X of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>POINT_Y</p> </td> <td> <p>Coordinate Y of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work, home, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>dep_time</p> </td> <td> <p>Departure time in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>trav_time</p> </td> <td> <p>Travel time to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:second</p> </td> </tr> <tr> <td> <p>trav_time_min</p> </td> <td> <p>Travel time in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>speed</p> </td> <td> <p>Travel speed to reach the activity location</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>distance</p> </td> <td> <p>Travel distance between the origin and the destination</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>act_start</p> </td> <td> <p>Start time of activity in minute (0-1439)</p> </td> <td> <p>Integer</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_time</p> </td> <td> <p>Activity duration in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_end</p> </td> <td> <p>End time of activity in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day given by MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a></p> <p>&nbsp;</p> <p><strong>(2) Travel trajectories</strong></p> <p>File name: 2_input_zip</p> <p>Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, &hellip;, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days.</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time in second in a simulation day (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation<sup>2</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Nearest road link consistent with (5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>vehicle</p> </td> <td> <p>Vehicle ID identical to person</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><sup>2 </sup>One typical episode of MATSim simulation events: Activity ends (actend) -&gt; Agent&rsquo;s vehicle enters traffic (vehicle enters traffic) -&gt; Agent&rsquo;s vehicle moves from previous road segment to its next connected one (left link) -&gt; Agent&rsquo;s vehicle leaves traffic for activity (vehicle leaves traffic) -&gt; Activity starts (actstart)</p> <p>&nbsp;</p> <p><strong>(3) Overnight charger access</strong></p> <p>File name: 3_home_charger_access.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>&nbsp;</p> <p><strong>(4) Car fleet composition</strong></p> <p>File name: 4_car_fleet.csv</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>income_class</p> </td> <td> <p>Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>&nbsp;</p> <p>(<strong>5) Road network with slope information</strong></p> <p>File name: 5_road_network_with_slope.shp (5 files in total)</p> <table> <tbody> <tr> <td> <p>Column</p> </td> <td> <p>Description</p> </td> <td> <p>Data type</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>length</p> </td> <td> <p>The length of road link</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>freespeed</p> </td> <td> <p>Free speed</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>capacity</p> </td> <td> <p>Number of vehicles</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>permlanes</p> </td> <td> <p>Number of lanes</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>oneway</p> </td> <td> <p>Whether the segment is one-way (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>modes</p> </td> <td> <p>Transport mode (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link_id</p> </td> <td> <p>Link ID</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>from_node</p> </td> <td> <p>Start node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>to_node</p> </td> <td> <p>End node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>count</p> </td> <td> <p>Aggregated traffic (number of cars travelled per day)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope in percent from -6% to 6%</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>LINESTRING (SWEREF99TM)</p> </td> <td> <p>geometry</p> </td> <td> <p>meter</p> </td> </tr> </tbody> </table> <p>&nbsp;</p> <p><strong>(6) Simulation scenarios specifying the parameter sets</strong></p> <p>File name: 6_scenarios.txt</p> <table> <tbody> <tr> <td> <p><strong>Parameter set</strong></p> <p><strong>(paraset)</strong></p> </td> <td> <p><strong>Strategy 1</strong></p> </td> <td> <p><strong>Strategy 2</strong></p> </td> <td> <p><strong>Strategy 3</strong></p> </td> <td> <p><strong>Fast charging power (kW)</strong></p> </td> <td> <p><strong>Minimum parking time for charging (min)</strong></p> </td> <td> <p><strong>Intermediate charging power (kW)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> </tbody> </table> <p>&nbsp;</p> <p><strong>(7) Time history of travel trajectories and charging of the simulated BEVs</strong></p> <p>File name: 7_output.zip</p> <p>Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days.</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>seq</p> </td> <td> <p>Sequence ID of time history by agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>purpose</p> </td> <td> <p>Valid for activities (home, work, school, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Link ID (link_id in File 5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>distance_driven</p> </td> <td> <p>Cumulative driven distance in the simulation day</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>energy_1</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_2</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_3</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>charger_1</p> </td> <td> <p>Power rating of the charger (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_2</p> </td> <td> <p>Power rating of the charger (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_3</p> </td> <td> <p>Power rating of the charger (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>soc_1</p> </td> <td> <p>State of charge (0-1, Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_2</p> </td> <td> <p>State of charge (0-1, Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_3</p> </td> <td> <p>State of charge (0-1, Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>&nbsp;</p>

opencc-by-4.0Feb 2023View details →
zenodo40/100

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&nbsp;scenarios applied to the case studies.</p>

opencc-by-4.0Sep 2023View details →
zenodo40/100

Vertical profiling of the electrical properties of charged desert dust during the pre-ASKOS campaign: Dataset

<p>The zipped files contain the datasets&nbsp;used to produce Figure 1 of the following conference proceedings paper:</p> <p>Vasiliki Daskalopoulou, George Hloupis, Sotirios A. Mallios, Ilias Makrakis, Evangelos Skoubris, Maria Kezoudi, Zbigniew Ulanowski, &amp; Vassilis Amiridis. (2021, July 6). <em>Vertical profiling of the electrical properties of charged desert dust during the pre-ASKOS campaign</em>. 15th International Conference on Meteorology, Climatology and Atmospheric Physics (COMECAP 2021), Ioannina, Greece. https://doi.org/10.5281/zenodo.5076042</p> <p>The repository contains overall:</p> <ol> <li>the ground-based JCI 131 Fieldmill Electrometer data that were acquired during the campaign (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Fieldmill_Ion_counter_Cyprus_campaign.rar">Fieldmill_Ion_counter_Cyprus_campaign.rar</a>)</li> <li>Data from an Alphalab Air Ion counter co-located with the fieldmill (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Fieldmill_Ion_counter_Cyprus_campaign.rar">Fieldmill_Ion_counter_Cyprus_campaign.rar</a>)</li> <li>Data from the five MiniMill electrometers that were launched (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/MiniMills_Cyprus_campaign.rar">MiniMills_Cyprus_campaign.rar</a>)</li> <li>Data from the two of the charge sensors that were launched (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Charge_sensors_Cyprus_campaign.rar">Charge_sensors_Cyprus_campaign.rar</a>)</li> <li>Data from the eleven ion counters that were launched, tethered together with the MiniMills or the charge sensors (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Ion_counters_Cyprus_campaign.rar">Ion_counters_Cyprus_campaign.rar</a>)</li> <li>A campaign calendar with the launches schedule (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Cyprus_campaign_November2019_calendar.pdf">Cyprus_campaign_November2019_calendar.pdf</a>).</li> </ol>

opencc-by-4.0Jul 2023View details →
zenodo36/100

Electric potential and charge density for null emission

<p>Fig6(c-d) plots electric potential and charge density for null emission. Parameters:  $m=5, \tau_{\rm p}=4, v_0=0, \phi_g=1$. </p>

opencc-by-4.0Jul 2017View details →
zenodo36/100

Nanotubes from the Misfit Layered Compound (SmS)1.19TaS2: Atomic Structure, Charge Transfer, and Electrical Properties_experimental dataset

<p>This dataset contains the raw experimental data for the Sreedhara et al.,&nbsp;Nanotubes from the Misfit Layered Compound (SmS)1.19TaS2: Atomic Structure, Charge Transfer, and Electrical Properties,&nbsp;<em>Chem. Mater.</em>&nbsp;2022, 34, 4, 1838&ndash;1853</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

Wide-field optical imaging of electrical charge and chemical reactions at the solid-liquid interface

<p>Code availability</p>

opencc-by-4.0Sep 2022View details →
zenodo36/100

Wide-field optical imaging of electrical charge and chemical reactions at the solid-liquid interface

<p>Data availability for polymer measurements</p>

opencc-by-4.0Sep 2022View details →
zenodo36/100

Wide-field optical imaging of electrical charge and chemical reactions at the solid-liquid interface

<p>Data availability of TiO2/SiO2 grid measurements</p>

opencc-by-4.0Sep 2022View details →
zenodo36/100

Electric vehicle occupancy of charging points in the city of Paris

<p>Dataset collected by EDF R&amp;D using the <em>Paris Data</em> open data platform, providing real-time occupancy of public charging points for electric vehicles in the city of Paris. This <strong>data.zip</strong> archive should be used at the root of the following gitlab repository (it replaces the empty data folder): <a href="https://gitlab.com/smarter-mobility-data-challenge/additional_materials">smarter-mobility-data-challenge/additional_materials</a>. V1 corresponds to the data provided for the Smarter Mobility Challenge, augmented with exogenous features such as weather and traffic. V2 corresponds to additional raw observations of occupancy data collected and accompanied by initial data processing.</p>

opencc-by-4.0Aug 2023View details →
zenodo36/100

Dataset: Magnetic and electric antennas calibration for partial discharge charge estimation in gas-insulated substations

<p>Data set for the publication named:&nbsp;Magnetic and electric antennas calibration for partial discharge charge estimation in gas-insulated substations</p>

opencc-by-4.0Oct 2023View details →
dryad36/100

Understanding the impact of public charging infrastructure on the consideration to purchase an electric vehicle in California

Open the record for dataset details and reuse information.

publicApr 2023View details →
dryad36/100

Urbanev: An open benchmark dataset for urban electric vehicle charging demand prediction

Open the record for dataset details and reuse information.

publicSep 2025View details →
zenodo32/100

Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling

<p>This supplementary material includes data and code for the research described in the paper &quot;Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling&quot;. The code containts an interface between the output files of the agent-based simulation model CURRENT and the energy system optimization model REMix as well as some scripts for analyzing REMix results. The data folder contains input data for REMix, the complete list of all model runs analyzed in the paper in the GAMS format .gdx as well as Excel files containing annual results of the sensitivity runs and respective pivot tables and figures for respective analysis.</p>

opencc-by-4.0Jan 2020View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record