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Elevate your Electric Vehicle (EV) development with Datatorq's comprehensive EV Data. We offer over 250 carefully curated and regularly updated data points, covering essential details like price, features, technical specifications, and dimensions.

Why choose Datatorq's Electric Vehicle (EV) Data? - Electric Vehicle (EV) Insights: Expert-curated data for optimal EV development and pricing. - Evolving Landscape: Stay ahead with in-depth insights. - Accurate Electric Vehicle (EV) Data: Clean, comprehensive, and up-to-date for confident decision-making. - Tailored Solutions: Get the exact Electric Vehicle (EV) data you need. - Monthly Updates: Stay current with the latest trends on the market.

Datatorq's expansive and precise Electric Vehicle (EV) datasets are designed to empower innovation and success in your EV product development and pricing strategies across Europe. Gain a competitive edge in France, UK, Italy, Poland, Netherlands, Spain, Belgium, Germany, Austria, Czechia, Portugal, Romania, Switzerland, Denmark, Norway, Slovenia, Sweden, and Ireland.

This dataset shows the Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) that are currently registered through Washington State Department of Licensing (DOL)

Number of Rows: 223,995 Number of Columns: 17 Contains Missing Values

Column Descriptions:

VIN (1-10): First 10 characters of the Vehicle Identification Number. County: The county where the vehicle is registered. City: The city where the vehicle is registered. State: The state where the vehicle is registered. Postal Code: The ZIP code of the vehicle's registration location. Model Year: The manufacturing year of the vehicle. Make: The brand/manufacturer of the vehicle (e.g., Tesla, Nissan). Model: The specific model of the vehicle. Electric Vehicle Type: The type of EV (Battery Electric Vehicle or Plug-in Hybrid). Clean Alternative Fuel Vehicle (CAFV) Eligibility: Indicates if the vehicle qualifies for CAFV benefits. Electric Range: The maximum range the vehicle can travel on a single charge. Base MSRP: The Manufacturer's Suggested Retail Price of the vehicle. Legislative District: The legislative district where the vehicle is registered. DOL Vehicle ID: A unique identifier assigned by the Department of Licensing. Vehicle Location: A general reference to the vehicle's location. Electric Utility: The electric utility company serving the vehicle's area. 2020 Census Tract: The census tract based on 2020 data for demographic analysis.

What can you do and learn from this data:

• Properly Engineer and Clean this dataset for better Analysis.
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• Fit a Machine learning Model, and improve accuracy.

This dataset contains technical specifications and performance criteria for various electric vehicle (EV) models. A total of 15 different EV models have been evaluated, each based on 20 different criteria. These criteria are categorized into cost and benefit criteria. Below is a detailed description of the key criteria included in the dataset:Price: The selling price of the vehicles, categorized as a cost criterion.Combined Consumption in Mild Weather: The energy consumption performance of the vehicles under mild weather conditions, categorized as a cost criterion.Acceleration: The time it takes for the vehicles to accelerate from 0 to 100 km/h, categorized as a benefit criterion.Top Speed: The maximum speed that the vehicles can achieve, categorized as a benefit criterion.Total Power: The total power output capacity of the vehicles, categorized as a benefit criterion.Total Torque: The maximum torque the vehicles can generate, categorized as a benefit criterion.Usable Battery Capacity: The usable battery capacity of the vehicles, categorized as a benefit criterion.Warranty Period: The warranty period offered for the vehicles, categorized as a benefit criterion.Charge Power (10-80%): The power capacity at which the vehicles can charge from 10% to 80%, categorized as a benefit criterion.Charge Time: The time required for the vehicles to reach a certain charge level, categorized as a cost criterion.Charge Speed: The speed at which the vehicles charge, categorized as a benefit criterion.WLTP Range: The driving range of the vehicles as determined by the Worldwide Harmonized Light Vehicles Test Procedure (WLTP), categorized as a benefit criterion.WLTP Rated Consumption: The energy consumption values of the vehicles according to WLTP standards, categorized as a cost criterion.Adult Occupant Safety: The safety performance of the vehicles for adult occupants, categorized as a benefit criterion.Child Occupant Safety: The safety performance of the vehicles for child occupants, categorized as a benefit criterion.Vulnerable Road Users Protection: The vehicles' performance in protecting vulnerable road users such as pedestrians and cyclists, categorized as a benefit criterion.Safety Assist: The safety assist systems provided by the vehicles, categorized as a benefit criterion.Maximum Payload: The maximum payload capacity of the vehicles, categorized as a benefit criterion.Cargo Volume: The cargo volume capacity of the vehicles, categorized as a benefit criterion.Unladen Weight (EU): The unladen weight of the vehicles as per EU standards, categorized as a cost criterion.This dataset provides a comprehensive overview of the various factors that can influence the decision-making process when selecting an electric vehicle, by balancing both cost and benefit criteria.

Dataset

This dataset was created by Umang Sachdev

Contents

The NHTSA Product Information Catalog and Vehicle Listing (vPIC) is a consolidated platform that presents data collected within the manufacturer reported data from CFR 49 Parts 551 - 574 for use in a variety of modern tools. NHTSA's vPIC platform is intended to serve as a centralized source for basic Vehicle Identification Number (VIN) decoding, Manufacturer Information Database (MID), Manufacturer Equipment Plant Identification and associated data. vPIC is intended to support the Open Data and Transparency initiatives of the agency by allowing the data to be freely used by the public without the burden of manual retrieval from a library of electronic documents (PDFs). While these documents will still be available online for viewing within the Manufacturer Information Database (MID) module of vPIC one can view and use the actual data through the VIN Decoder and Application Programming Interface (API) modules.

This layer shows household size by number of vehicles available. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 7, 2023The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2022 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Datatorq’s LCV Car Spec Data is your secret weapon for exceptional product development. Get access to over 250 detailed data points, including price, equipment, specifications, and dimensions. Our data is meticulously refined and updated monthly to ensure your LCV strategy is always ahead of the competition.

Why Choose Datatorq’s Car Data? - Precision Product Development: Get the insights you need to optimize your LCV products and pricing. - Unmatched Expertise: Benefit from our industry-leading LCV market knowledge. - Reliable Information: Count on clean, complete, and accurate data. - Tailored Solutions: Get exactly what you need with our customizable options. - Always Current: Stay ahead with our monthly Car Spec Data updates.

Datatorq's comprehensive and accurate LCV Car Spec Data empowers innovation and achievement in product and pricing strategies across the world (France, UK, Italy, Poland, Netherlands, Spain, Belgium, Germany, Austria, Czechia, Portugal, Romania, Switzerland, Bulgaria, Croatia, Denmark, Hungary, Norway, Slovenia, Sweden, Ireland, Turkey, Morocco, Brazil, Argentina, Colombia, Mexico, Australia).

Datová sada obsahuje data kompletních technických údajů vozidel registrovaných v Registru silničních vozidel.

A pan-european API for car number plate lookups. Our API is a .NET ASMX webservice, that allows you connect from any programming environment, either .NET (C#, Visual Basic .NET), or through any programming language that supports SOAP (PHP nuSoap, Python, Ruby, Java etc.). Register for free and we will set you up with a test account, where you can make 10 vehicle lookups for free. If you require higher volume of lookups, then our fee is €0.20 per lookup, purchased in blocks of a minimum of 100, a 10% discount is available for packages over 1000.

This layer shows household size by number of vehicles available. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The CVS Database provides a catalogue of original vehicle dimensions, for use in vehicle safety research and collision investigation. The purpose of this database is to provide users with a comprehensive listing of vehicle dimensions commonly used in the field of collision investigation and reconstruction, for the North American fleet of passenger cars, light trucks, vans and SUV’s. The database includes model years dating back to 2011 and is comprised of both commonly available dimensions such as overall length, wheelbase and track widths, and also several dimensions which are not typically readily available from the manufacturers, nor from automotive publications. Note – To obtain database of model years dating back to 1971, please contact Transport Canada.

Imports - Trucks, Buses & Spec-Purpose Vehicles (Census Basis) in the United States increased to 6025.83 USD Million in February from 5924.78 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Imports of Trucks, Buses & Spec-purpose Vehicles.

This database gives an overview of the specifications of 93 unmanned aerial vehicles (UAVs). This database can be used to characterize different types of UAVs and their properties. The database contains the following information for each UAV: - Maximum takeoff weight (MTOW) - Payload - Wingspan - Lenght - Cruise speed - Maximum speed - Stall speed - Range - Endurance - Altitude - Airfoil - Aspect ratio - Chord length (estimated) - Propeller diameter - UAV type

Three Variants of Circuits on Driving Dynamics Area of KIT "Campus Ost"

The English version can be found below.

Einleitung

Die drei verschiedenen Kurse (track_01, track_02, track_03) unterscheiden sich in einzelnen Abschnitten, sodass je nach Variante z.B. engere Kurven vermieden werden oder längere gerade Abschnitte auftreten. Die Kurse finden in verschiedenen Projekten des Instituts für Fahrzeugsystemtechnik (FAST) und des Schaeffler Hub for Advanced Research am KIT (SHARE am KIT) Anwendung, beispielsweise zur Präsentation der Projektergebnisse im Projekt SmartLoad oder zur Erforschung neuer Ansätze zur Fahrzeugführungsregelung (Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay). Der Datensatz enthält die Kurse zum einen in Form einer Pfad-Definition als Input für einen Pfadfolgeregler und zum anderen als Straßen/Szenario-Definition zur Verwendung in der Gesamtfahrzeugsimulation mit CarMaker.

Format der Pfaddefinition

Die Pfade werden definiert über ein Matlab mat-File in Form eines Structure Array, in welchem diskrete Stützstellen des Pfades enthalten sind. Als Diskretisierung wurde ein Abstand zwischen den Stützstellen von ungefähr 0.3m gewählt. Das verwendete Koordinatensystem, in dem die Koordinaten der Stützstellen definiert sind, ist ein rechtshändiges kartesisches Koordinatensystem, dessen z-Achse nach oben zeigt. Dieses hat seinen Ursprung bei den globalen Koordinaten 49.022732549318°N, 8.432433939454015°E. Die X-Achse zeigt in Richtung des Punktes mit den Koordinaten 49.02303283495321°N, 8.431491841757758°E. An jeder Stützstelle sind folgenden Dimensionen des Pfades definiert: - X-Koordinate in m - Y-Koordinate in m - Referenzgeschwindigkeit v in m/s - Pfad-Koordinate s in m - Bahnkrümmung in 1/m - Tangentiale Pfadorientierung in rad

Rundkurse in CarMaker

Die Tracks können in die Gesamtfahrzeugsimulation mit CarMaker eingebunden werden. Somit kann beispielsweise ein Fahrzeugführungsregler anhand der Rundkurse simulativ optimiert werden und später in der Realität auf dem Testgelände validiert werden. Die Tracks sind in zwei unterschiedlichen Arten modelliert. Zum einen als einspurige Straße (Index „Road“) und zum anderen als Mittellinie auf einer Dynamikfläche (Index „Line“). Letztere Variante lässt bei der Simulation größere Abweichungen vom Soll-Pfad zu, ohne dass es zum Abbruch des Simulationsdurchlaufes durch CarMaker kommt, womit sich diese Variante besser zur Entwicklung von Regler-Algorithmen eignet. Die Integration in ein CarMaker Projekt erfolgt durch Kopieren der zwölf Dateien (Track_01_Line, Track_01_Line.rd5; Track_01_Road; Track_01_Road.rd5;…) in den CarMaker Projektordner …\Data\Road. In der CarMaker GUI kann der Kurs dann unter Parameters -> Scenario/Road -> Load Road file ausgewählt werden. Die erstellten .rd5 Files basieren auf der CarMaker Version 8.0.

English version:

Introduction

The three different tracks (track_01, track_02, track_03) differ in some sections, so that depending on the variant, e.g. tighter curves are avoided or longer straight sections occur. The circuits are used in various projects of the Institute of Vehicle System Technology (FAST) and the Schaeffler Hub for Advanced Research at KIT (SHARE at KIT), for example to present project results in the SmartLoad project or to research new approaches for vehicle guidance control (Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay). The dataset contains the circuits in the form of a path definition as input for a path tracking controller and as a road/scenario definition for use in the vehicle dynamics simulation tool CarMaker.

Format of the path definition files

The paths are defined using a Matlab mat-file in the form of a structure array containing discrete grid points of the path. As discretization a distance between the grid points of about 0.3m was chosen. The coordinate system used to define the grid points is a right-handed cartesian coordinate system with the z-axis pointing upwards. It has its origin at the global coordinates 49.022732549318°N, 8.432433939454015°E. The x-axis points in the direction of the coordinates 49.02303283495321°N, 8.431491841757758°E. The following dimensions of the path are defined at each grid point: - X-coordinate in m - Y-coordinate in m - Reference velocity v in m/s - Path-coordinate s in m - Path curvature in 1/m - Tangential path orientation in rad

Tracks in CarMaker

The tracks can be integrated into the vehicle dynamics simulation tool CarMaker. For example, a vehicle guidance controller can be simulatively optimized using the paths and later validated in reality on the driving dynamics area. The tracks are modeled in two different ways. On the one hand as a single-lane road (index "Road") and on the other hand as a center line on a driving dynamics area (index "Line"). The latter variant allows larger deviations from the target path during the simulation without causing CarMaker to abort the simulation run, which makes this variant more suitable for the development of controller algorithms. The integration into a CarMaker project is done by copying the twelve files (Track_01_Line, Track_01_Line.rd5; Track_01_Road; Track_01_Road.rd5;...) into the CarMaker project folder ...\Data\Road. In the CarMaker GUI the track can be selected under Parameters -> Scenario/Road -> Load Road file. The created .rd5 files are based on CarMaker version 8.0.

Dataset

This dataset was created by valcho valev

Contents

Fast cars : packed with facts, technical specs and breathtaking imagery is a book. It was written by Robin Brown and published by Igloo Books in 2017.

The NHTSA Product Information Catalog and Vehicle Listing (vPIC) is a consolidated platform that presents data collected within the manufacturer reported data from CFR 49 Parts 551 - 574 for use in a variety of modern tools. NHTSA's vPIC platform is intended to serve as a centralized source for basic Vehicle Identification Number (VIN) decoding, Manufacturer Information Database (MID), Manufacturer Equipment Plant Identification and associated data. vPIC is intended to support the Open Data and Transparency initiatives of the agency by allowing the data to be freely used by the public without the burden of manual retrieval from a library of electronic documents (PDFs). While these documents will still be available online for viewing within the Manufacturer Information Database (MID) module of vPIC one can view and use the actual data through the VIN Decoder and Application Programming Interface (API) modules.

In addition to new registrations of motor vehicles and their trailers, the registration authorities in Germany also notify the Federal Motor Transport Authority (KBA) of the decommissioning of vehicles. These communications on motor vehicles and their trailers and their holders are stored in the Central Vehicle Register (ZFZR) and form the data basis for the stock of motor vehicles and their trailers. This product shows the ''stock of motor vehicles and their trailers by manufacturer and type''. It includes all vehicle types for which a KBA manufacturer key and a national type key were assigned on the basis of type approval and which were thus entered in the Central Vehicle Register (ZFZR) at the time of evaluation. Vehicles for which this information is missing are not taken into account. This includes, inter alia, vehicles registered as part of an individual assessment or which, due to technical modifications, no longer meet the specifications of the original type-approval. The censuses of the vehicle stock are carried out with the reference date of 1 January of each year. Evaluated for the first time on 1 January 2019.