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).

Introduction

Electric Vehicle Statistics: An electric vehicle, sometimes known as an EV. It is a type of vehicle that is propelled by one or more electric motors or traction motors.

Unlike classic ICE vehicles that run on gasoline or diesel. Electric vehicles (EVs) are powered by energy stored in batteries or supplied by an external power source.

The primary energy source for EVs is electricity, converted into mechanical energy to drive the vehicle. Different types of electric vehicles cater to various needs and preferences. Offering options for emissions reduction, energy efficiency, and varying ranges.

As technology advances, the electric vehicle landscape evolves, with more models and variations becoming available to consumers.

https://scoop.market.us/wp-content/uploads/2023/08/Electric-Vehicles-Statistics.png" alt="Electric Vehicle Statistics" class="wp-image-37088">

Until 2020, medium cars were the most popular segment within the battery-electric vehicle (BEV) market. Around 41 percent of all-electric cars sold worldwide in 2020 were medium-sized vehicles. By 2023, sport-utility vehicles had become the best-selling segment, amounting to 45 percent of all BEV global sales.

This shows the number of electric vehicles that were registered by Washington State Department of Licensing (DOL) each month. DOL integrates National Highway Traffic Safety Administration (NHTSA) data and the Environmental Protection Agency (EPA) fuel efficiency ratings with DOL titling and registration data to create this information.

Cars with an electrified engine are tipped to account for just under ********** of the global market by 2025. It is estimated that pure battery electric vehicles will account for about *** percent of worldwide car sales. Internal combustion engines are set to lose market share It is expected that the market share of conventional internal combustion engines will shrink to about ** percent by 2050, while electric vehicles are projected to account for ***** out of ten vehicle sales. Growth in pure battery electric vehicles’ market share shows consumer preference set on fully electric cars. Overall, rising popularity of electrified vehicles could prove vital in carbon dioxide mitigation. Electrified vehicles include cars that may use an electric motor when less power is needed and the main engine could be switched off. Electrified vehicles are increasingly becoming more competitive Hybrids have been preferred over battery electric vehicles due to the much larger range of fuel propelled vehicles, but enhanced battery technology of electric vehicle range continues to narrow this gap. Batteries are now also able to power larger cars such as SUVs, enabling new demographics to be targeted.

CONTEXT: This is a dataset of electric vehicles.

One of the more popular data science datasets is the mtcars dataset. It is known for its simplicity when running analysis and visualizations.

When looking for simple datasets on EVs, there don't seem to be any. Also, given the growth in this market, this is something many would be curious about. Hence, the reason for creating this dataset.

For more information, please visit the data source below.

TASKS: Some basic tasks would include 1. Which car has the fastest 0-100 acceleration? 2. Which has the highest efficiency? 3. Does a difference in power train effect the range, top speed, efficiency? 4. Which manufacturer has the most number of vehicles? 5. How does price relate to rapid charging?

CONTENT: I've included two datasets below:

1. 'ElectricCarData_Clean.csv' -- original pulled data.

2. 'ElectricCarData_Norm.csv' -- units removed from each of the rows -- rapid charge has a binary yes/no value

The point of both is to have users practice some data cleaning.

CREDITS: There are two credits and sourcing that needs to be mentioned: 1. Datasource: ev-database.org/ 2.*Banner image*: freepik - author - 'macrovector'

UPDATES: There will be future updates when we can attain additional data.

Electric Vehicles in India

Here is a list of currently available electric vehicles in India. This list also includes:

• Vehicle Style
• Range
• Transmission
• Vehicle Type (mostly electric of course ! 😃 )
• Price Range (ex Delhi)
• Boot Space
• Base Model
• Top Model

In 2023, Poland's number of battery-electric (BEV) cars in Poland reached nearly ******. It is expected that by 2030, their number will increase to *******. Electric cars in Poland Electric cars are gaining popularity, driven by growing climate awareness and, in part, by government subsidies. The electric vehicle market has seen significant growth recently. While only *** percent of newly registered vehicles in 2021 were electric, forecasts predict that by 2030, nearly one-fifth of all new registrations will be electric vehicles. German battery-electric vehicles were especially popular, with four of the five best-selling brands originating from Germany. However, the list was led by the American electric car manufacturer Tesla. Infrastructure for electric cars in Poland Many people considered the infrastructure for electric cars important, as they often had a relatively short range. The number of charging stations in Poland rose significantly between 2020 and 2023. Medium-speed charging stations increased from just over 1,000 in 2020 to ***** by 2023. In 2023, most stations and charging points were in the Mazowieckie voivodeship, followed by the Śląskie voivodeship.

This shows records of title activity (transactions recording changes of ownership) and registration activity (transactions authorizing vehicles to be used on Washington public roads).

This dataset includes all new electric vehicles registered in Connecticut from 1/1/2021 to the most recent data available. The data is updated bi-annually.

The Electric Cars Market is segmented by Vehicle Configuration (Passenger Cars), by Fuel Category (BEV, FCEV, HEV, PHEV) and by Region (Africa, Asia-Pacific, Europe, Middle East, North America, South America). The report offers market size in both market value in USD and market volume in unit. Further, the report includes a market split by Vehicle Type, Vehicle Configuration, Vehicle Body Type, Propulsion Type, and Fuel Category.

The global electric car market size was valued at USD 178.1 Billion in 2024. Looking forward, IMARC Group estimates the market to reach USD 648.8 Billion by 2033, exhibiting a CAGR of 15.45% from 2025-2033. Asia Pacific currently dominates the market, holding the largest market share in 2024. The electric car market share is increasing due to the rising environmental awareness among consumers, strict emission standards put in place by various governments around the globe, and advancements made in battery technology and charging infrastructure. emissions standards by governments across the globe, and the advancements in battery technology and charging infrastructure are some of the major factors propelling the market.

SpaceKnow uses satellite (SAR) data to capture activity in electric vehicles and automotive factories.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data that monitors the area which is covered with assembled light vehicles in square meters.

We offer 3 delivery options: CSV, API, and Insights Dashboard

Available companies Rivian (NASDAQ: RIVN) for employee parking, logistics, logistic centers, product distribution & product in the US. (See use-case write up on page 4) TESLA (NASDAQ: TSLA) indices for product, logistics & employee parking for Fremont, Nevada, Shanghai, Texas, Berlin, and Global level Lucid Motors (NASDAQ: LCID) for employee parking, logistics & product in US

Why get SpaceKnow's EV datasets?

Monitor the company’s business activity: Near-real-time insights into the business activities of Rivian allow users to better understand and anticipate the company’s performance.

Assess Risk: Use satellite activity data to assess the risks associated with investing in the company.

Types of Indices Available Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices. The first one is CFI-R which gives you level data, so it shows how many square meters are covered by metallic objects (for example assembled cars). The second one is CFI-S which gives you change data, so it shows you how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Product index This index monitors the area covered by manufactured cars. The larger the area covered by the assembled cars, the larger and faster the production of a particular facility. The index rises as production increases.

Product distribution index This index monitors the area covered by assembled cars that are ready for distribution. The index covers locations in the Rivian factory. The distribution is done via trucks and trains.

Employee parking index Like the previous index, this one indicates the area covered by cars, but those that belong to factory employees. This index is a good indicator of factory construction, closures, and capacity utilization. The index rises as more employees work in the factory.

Logistics index The index monitors the movement of materials supply trucks in particular car factories.

Logistics Centers index The index monitors the movement of supply trucks in warehouses.

Where the data comes from: SpaceKnow brings you information advantages by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the EV industry with just a 4-6 day lag, on average.

The EV data help you to estimate the performance of the EV sector and the business activity of the selected companies.

The backbone of SpaceKnow’s high-quality data is the locations from which data is extracted. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

Each individual location is precisely defined so that the resulting data does not contain noise such as surrounding traffic or changing vegetation with the season.

We use radar imagery and our own algorithms, so the final indices are not devalued by weather conditions such as rain or heavy clouds.

→ Reach out to get a free trial

Use Case - Rivian:

SpaceKnow uses the quarterly production and delivery data of Rivian as a benchmark. Rivian targeted to produce 25,000 cars in 2022. To achieve this target, the company had to increase production by 45% by producing 10,683 cars in Q4. However the production was 10,020 and the target was slightly missed by reaching total production of 24,337 cars for FY22.

SpaceKnow indices help us to observe the company’s operations, and we are able to monitor if the company is set to meet its forecasts or not. We deliver five different indices for Rivian, and these indices observe logistic centers, employee parking lot, logistics, product, and prod...

This dataset contains data on the charging time of electric vehicles for a typical month in a certain city. The types of electric vehicles include: buses, private passenger cars, ride hailing vehicles, logistics vehicles, and rental passenger cars. The activity area is divided into: office area, industrial area, residential area, and commercial area. The dataset takes one hour as the statistical cycle to calculate the charging frequency of electric vehicles of various types and regions during a certain period of time. Methods This dataset is selected from the electric vehicle charging station status statistical dataset provided by Henan Power Grid. When organizing this dataset, unnecessary status data was removed and October was taken as a typical month, and the charging data of electric vehicles in October was extracted separately.

This dataset contains detailed information on electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) registered in the United States. It includes data about vehicle characteristics, location, and eligibility for clean alternative fuel vehicle (CAFV) programs. The dataset tracks key attributes such as VIN, make, model, model year, and electric vehicle type (e.g., Battery Electric Vehicle, Plug-in Hybrid Electric Vehicle). Additionally, it provides insights into the vehicle's eligibility for various clean fuel incentives and its battery range, where available.

Key Features: VIN (1-10): A unique identifier for each vehicle.

County, City, State, Postal Code: Geographical location of the vehicle's registration.

Model Year: The year the vehicle was manufactured.

Make & Model: Manufacturer and specific model of the vehicle.

Electric Vehicle Type: Indicates whether the vehicle is a Battery Electric Vehicle (BEV) or a Plug-in Hybrid Electric Vehicle (PHEV).

Clean Alternative Fuel Vehicle (CAFV) Eligibility: Details whether the vehicle qualifies for clean fuel vehicle programs.

Battery Range Information: Whether the vehicle's battery range has been researched and its eligibility for clean fuel incentives.

Price Ranges: The price categories of vehicles based on their range.

Insights and Use Cases: Vehicle Popularity Analysis: Analyze which EV models (e.g., Tesla, Nissan LEAF) are more prevalent across various U.S. regions.

CAFV Eligibility: Investigate how many electric vehicles are eligible for clean alternative fuel incentives and the distribution of these vehicles.

Geographic Distribution: Study regional patterns of EV adoption, including differences between urban and rural locations.

EV Type Analysis: Compare the number of Battery Electric Vehicles (BEVs) versus Plug-in Hybrid Electric Vehicles (PHEVs) in the dataset.

Market Trend Analysis: Track EV sales trends over the years and understand the impact of policy changes or environmental factors on EV adoption.

Potential Applications: Market Research: Analyze the geographic distribution of electric vehicles to identify areas with high adoption rates and potential for growth.

Policy Impact Assessment: Evaluate the effect of incentives on the adoption of electric vehicles.

Environmental Impact Studies: Assess the environmental benefits of electric vehicle adoption based on the vehicle type and eligibility for clean fuel programs.

This dataset is perfect for those interested in exploring trends in electric vehicle adoption, understanding market dynamics, and performing predictive analysis for the EV industry.

The data consists of normal driving records for dozens of private cars over several months (from June 5, 2015 to June 30, 2016), with a sampling frequency of one minute. The basic specifications of the vehicles are as follows: Roewe E50 is a pure electric vehicle, weighing 1080 kilograms. It is equipped with a 22.4 kWh battery pack, and is reported to have a driving range of 170 kilometers. The raw data has been preprocessed and denoised, resulting in a final dataset containing 10,000 trips. This dataset offers substantial potential for reuse in research and analysis focused on electric vehicle energy consumption. Researchers, engineers, and policy makers can leverage this data to understand patterns, develop optimization algorithms, and inform energy-efficient practices. The dataset adheres to all applicable legal requirements. All sensitive information has been removed, and the data has been preprocessed to ensure confidentiality. There are no known legal or ethical obstacles to its use.

This dataset contains session details from publicly available, Town-owned electric vehicle charging stations. The dataset does not include the EV charging station located at Herb Young Community Center Parking Deck (121 Wilkinson Avenue Cary, NC 27513) although it is operational. This report was pulled January 3, 2023. The dataset is updated monthly.

Background

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 "Liquid-fuel" 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's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc.

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).

https://doi.org/10.1016/j.trd.2023.103645

Methodology

This data product is centered on the 1.7 million inhabitants of the Västra Götaland (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.

This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' 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 & 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).

Data description

The current data product covers seven data files.

(1) Agents' experienced activity plans

File name: 1_activity_plans.csv

Column

Description

Data type

Unit

person

Agent ID

Integer

-

act_id

Activity index of each agent

Integer

-

deso

Zone code of Demographic statistical areas (DeSO)1

String

-

POINT_X

Coordinate X of activity location (SWEREF99TM)

Float

meter

POINT_Y

Coordinate Y of activity location (SWEREF99TM)

Float

meter

act_purpose

Activity purpose (work, home, other)

String

-

mode

Transport mode to reach the activity location (car)

String

-

dep_time

Departure time in decimal hour (0-23.99)

Float

hour

trav_time

Travel time to reach the activity location

String

hour:minute:second

trav_time_min

Travel time in decimal minute

Float

minute

speed

Travel speed to reach the activity location

Float

km/h

distance

Travel distance between the origin and the destination

Float

km

act_start

Start time of activity in minute (0-1439)

Integer

minute

act_time

Activity duration in decimal minute

Float

minute

act_end

End time of activity in decimal hour (0-23.99)

Float

hour

score

Utility score of the simulation day given by MATSim

Float

-

1 https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/

(2) Travel trajectories

File name: 2_input_zip

Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days.

Column

Description

Data type

Unit

time

Time in second in a simulation day (0-86399)

Integer

Second

type

Event type defined by MATSim simulation2

String

-

person

Agent ID

Integer

-

link

Nearest road link consistent with (5)

String

-

vehicle

Vehicle ID identical to person

Integer

-

2 One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)

(3) Overnight charger access

File name: 3_home_charger_access.csv

Column

Description

Data type

Unit

person

Agent ID

Integer

-

home_charger

Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)

Integer

-

(4) Car fleet composition

File name: 4_car_fleet.csv

Column

Description

Data type

Unit

person

Agent ID

Integer

-

income_class

Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)

Integer

-

car

Car model class (B=40 kWh, C=60 kWh, D=100 kWh)

String

-

(5) Road network with slope information

File name: 5_road_network_with_slope.shp (5 files in total)

Column

Description

Data type

Unit

length

The length of road link

Float

meter

freespeed

Free speed

Float

km/h

capacity

Number of vehicles

Integer

-

permlanes

Number of lanes

Integer

-

oneway

Whether the segment is one-way (0=no, 1=yes)

Integer

-

modes

Transport mode (car)

String

-

link_id

Link ID

String

-

from_node

Start node of the link

String

-

to_node

End node of the link

String

-

count

Aggregated traffic (number of cars travelled per day)

Integer

-

slope

Slope in percent from -6% to 6%

Float

-

geometry

LINESTRING (SWEREF99TM)

geometry

meter

(6) Simulation scenarios specifying the parameter sets

File name: 6_scenarios.txt

Parameter set

(paraset)

Strategy 1

Strategy 2

Strategy 3

Fast charging power (kW)

Minimum parking time for charging (min)

Intermediate charging power (kW)

0

0.2

0.2

0.9

150

5

22

1

0.2

0.2

0.9

50

5

22

2

0.3

0.3

0.9

150

5

22

3

0.3

0.3

0.9

50

5

22

(7) Time history of travel trajectories and charging of the simulated BEVs

File name: 7_output.zip

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.

Column

Description

Data type

Unit

person

Agent ID

Integer

-

home_charger

Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)

Integer

-

car

Car model class (B=40 kWh, C=60 kWh, D=100 kWh)

String

-

seq

Sequence ID of time history by agent

Integer

-

time

Time (0-86399)

Integer

Second

purpose

Valid for activities (home, work, school,

Total number of electric and plug-in hybrid vehicle registrations by county as of each month end from July 2020 to May 2025.