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).

Young people between 18 and 29 years old were the age group most likely to consider the purchase of an electric vehicle in the United States as of May 2022. Around ** percent of the Americans between 18 and 29 years old surveyed reported being somewhat or very likely to consider buying an electric car. By contrast, only ** percent of the respondents over 65 years old surveyed declared having an interest for the vehicle segment. The likelihood to consider the purchase of an electric vehicle also depended on consumers' previous EV knowledge.

In the third quarter of 2025, over ******* battery-electric vehicles were sold in the United States. This was a year-over-year increase of over ***percent compared to the sales recorded in the third quarter of 2024. The third quarter of 2025 also recorded a hike in sales compared to the second quarter of that same year, making it the best quarter for BEV sales in the country ever. The stark increase in sales in the third quarter of 2025 is tied to the elimination of EV purchase incentives by the U.S. government. The fourth quarter of 2025 could very well see a decrease in sales. Global EV Race - Where does the U.S. stand? Over the last few years, consumers have perceived Electric Vehicles (EVs) as a far more appealing option due to their increased range, battery life, variety of models, and affordability. Therefore, the EV market has grown fast in recent years and is forecast to expand to ****billion U.S. dollars in 2029. Though the global demand for electric cars has been escalating, American sales lag behind Europe and the Asia-Pacific regions. In 2024, Chinese customers bought around **** million plug-in EVs, considerably more than American customers' purchases, around *** million that year. China leads the global EV race, with a substantial **** percent growth in sales year-on-year in 2024. However, given the market share of electric vehicles in the global automotive industry, this still can be anyone's race. Outlook of the U.S. market There is still a lack of interest in electric vehicles among American buyers compared to European and Asian consumers. In the first half of 2021, the share of the battery electric vehicle was **** percentage points more in Norway than in the U.S. One of the main reasons is that American consumers still anticipate that EVs are more expensive than gasoline vehicles and diesel internal combustion engine cars (ICE). This perception is partially true in the U.S. since the battery production market is highly concentrated in Asia, where the companies have logistical advantages, leading automotive makers to offer better prices. On the other hand, high licensing fees for electric vehicles are another factor affecting the consumption behaviors of automobile purchasers. In many states, the licensing fees for electric cars are considerably higher than their ICE counterparts. EV licensing fees were around *** U.S. dollars compared to ** U.S. dollars for standard vehicles in Georgia in 2021. Together, these factors significantly impact the individual perception of electric cars in the United States.

The number of all-electric cars in operation in Germany has picked up steam over the past decade and soared to new heights at the beginning of 2025, when more than **** million battery-electric cars were registered in Germany. Given Germany's large appetite for diesel and petrol cars, the growing share of plug-in electric vehicles in new registrations is striking. About **** percent of cars sold in 2024 in Germany were battery-electric cars, and another *** percent were plug-in hybrid electric vehicles. New car registrations Passenger car sales in Germany dropped amid the COVID-19 pandemic and global automotive semiconductor shortage but have increased slightly in 2023, up to *** million units sold. Annual sales of battery and plug-in hybrid electric vehicles came close to ******* units in Germany, making it the largest market in terms of absolute plug-in electric car registrations in Europe in 2024. Tesla Model 3 most popular model in Europe The Tesla Model Y was the best-selling plug-in electric vehicle model in Europe in 2024. That year, Tesla sold almost ******* units of its Model Y electric car to customers in Europe. Tesla's Model 3 and Škoda's Enyaq rounded off the top three in the ranking.

📝 Description

This Electric Vehicle (EV) Sales and Adoption dataset contains detailed records of electric vehicle sales, including vehicle details, region, customer segments, and sales metrics. It aims to help data enthusiasts and businesses forecast EV sales, analyze market trends, and derive insights to improve marketing and inventory strategies.

📊 Collection Methodology

Data Aggregation: Combined from (fictional) public EV registration records, automotive dealership sales reports, and online retailer transactions.

Quality Control: Only confirmed EV transactions are included; partially-completed orders and canceled orders were filtered out.

Revenue Calculation: Reflects the final sale price after applying any applicable discounts or incentives.

Feature Engineering: Customer demographics (segment, region) are included to facilitate market segmentation analysis.

🚀 Potential Use Cases

Sales Forecasting – Predict future EV sales volume based on regional and demographic patterns.

Market Trend Analysis – Identify which brands and vehicle types are most popular in specific regions.

Battery and Range Insights – Correlate battery capacity and fast-charging options with sales performance.

Consumer Behavior & Segmentation – Understand different customer segments' purchasing habits and price sensitivities.

Environmental Policy & Incentive Impact – Investigate how discounts or tax incentives affect adoption rates.

🔑 Key Features

Date: Represents a month in YYYY-MM format.

Region: Geographic region where sales took place.

Brand: Automotive brand (e.g., Tesla, BYD, Volkswagen, etc.).

Model: Specific EV model name.

Vehicle_Type: Category (Sedan, SUV, Hatchback, etc.).

Battery_Capacity_kWh: Battery capacity in kilowatt-hours.

Discount_Percentage: Any discount applied to final sale (%).

Customer_Segment: Broad segmentation (High Income, Tech Enthusiast, Eco-Conscious, etc.).

Fast_Charging_Option: Indicates if the vehicle supports fast-charging.

Units_Sold: Total number of units sold (in train.csv).

Revenue: Total revenue from units sold (in train.csv).

This dataset is well-suited for machine learning, statistical analysis, and data visualization projects that address growing interest in electrification, sustainability, and emerging transportation technologies!

This shows the number of vehicles that were registered by Washington State Department of Licensing (DOL) each month. The data is separated by county for passenger vehicles and trucks. 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.

This dataset shows the number of vehicles that were registered by Washington State Department of Licensing (DOL) each month. The data is separated by county for passenger vehicles and trucks.

BYD was ranked as the best-selling electric vehicle manufacturer worldwide after selling over ************ units in 2024 after overtaking Tesla as the best-selling electric vehicle manufacturer in the previous year. BYD's sales volume translates into a market share of nearly ********* of the market. Tesla and the Geely-Volvo Car Group were among the runners-up. Plug-in electric vehicles explained Plug-in electric vehicle registrations have been on the rise in 2023, with the Chinese electric vehicle market growing by ** percent year-over-year. There are two types of plug-in electric vehicles: battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV). Both contain a charging socket and a rechargeable battery that can power the vehicle on its own. However, a PHEV also includes a traditional internal combustion engine, which kicks in should the battery reach a low level. Therefore, a BEV is the only zero-emission vehicle. BEVs are a growing market, with global 2023 sales over ****** 2021 sales. What type of electric vehicles do producers build? Manufacturers such as SAIC and Bayerische Motoren Werke (BMW) produce both, battery-electric and plug-in hybrid electric vehicles, but Tesla exclusively builds all-electric vehicles. The American company launched its first battery-powered car in 2008, the Roadster. In 2024, Tesla's model line-up included Model S, Model 3, Model X, and Model Y vehicles, as well as the Cybertruck. The Model Y was the best-selling plug-in electric vehicle model worldwide, though most of the leading ten PEV models were from Chinese brands, reflecting the fast-paced EV market in Asia-Pacific.

ABOUT

The Electric Vehicle Dataset provides a detailed record of registered electric vehicles (EVs), including key attributes such as make, model, electric range, location, and eligibility for clean alternative fuel programs. This dataset is valuable for researchers, policymakers, automotive analysts, and sustainability advocates interested in studying the adoption of EVs, regional distribution, and policy impacts on electric mobility.

Key features of the dataset:

✅ Covers multiple manufacturers and EV models
✅ Includes vehicle registration details across different regions
✅ Provides insights into electric range, price, and policy eligibility
✅ Useful for analyzing EV adoption trends and infrastructure planning

This dataset serves as a crucial resource for understanding electric vehicle usage, tracking policy effectiveness, and supporting clean energy initiatives.

FILE INFORMATION

The dataset consists of structured records related to registered electric vehicles, compiled from various sources. Each file contains details such as manufacturer, model year, electric range, and location data, making it useful for studying EV market trends and adoption rates. The file format is CSV.

COLUMNS DESCRIPTION

• VIN (1-10) – The 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 postal code of the vehicle’s registered location.
• Model Year – The production year of the vehicle.
• Make – The manufacturer of the electric vehicle (e.g., Tesla, Nissan).
• Model – The specific model of the EV (e.g., Model 3, Leaf).
• Electric Vehicle Type – The classification of the vehicle (e.g., Battery Electric Vehicle, Plug-in Hybrid).
• Clean Alternative Fuel Vehicle (CAFV) Eligibility – Indicates whether the vehicle qualifies for clean alternative fuel programs.
• Electric Range – The estimated driving range on a full charge (in miles or kilometers).
• Base MSRP – The Manufacturer’s Suggested Retail Price (market price of the vehicle).
• Legislative District – The legislative district where the vehicle is registered.
• Electric Utility – The electricity provider in the vehicle’s registered location.

This dataset is ideal for analyzing electric vehicle market penetration, regional distribution, and policy effectiveness in promoting clean transportation.

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.

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).

A Battery Electric Vehicle (BEV) is an all-electric vehicle using one or more batteries to store the electrical energy that powers the motor and is charged by plugging the vehicle in to an electric power source. A Plug-in Hybrid Electric Vehicle (PHEV) is a vehicle that uses one or more batteries to power an electric motor; uses another fuel, such as gasoline or diesel, to power an internal combustion engine or other propulsion source; and is charged by plugging the vehicle in to an electric power source.

Clean Alternative Fuel Vehicle (CAFV) Eligibility is based on the fuel requirement and electric-only range requirement as outlined in RCW 82.08.809 and RCW 82.12.809 to be eligible for Alternative Fuel Vehicles retail sales and Washington State use tax exemptions. Sales or leases of these vehicles must occur on or after 8/1/2019 and meet the purchase price requirements to be eligible for Alternative Fuel Vehicles retail sales and Washington State use tax exemptions.

Monthly count of vehicles for a county may change from this report and prior reports. Processes were implemented to more accurately assign county at the time of registration.

Updated: March 12, 2024

This dataset contains detailed information about electric vehicles (EVs) registered in the state of Washington. It includes vehicle specifications, electric range, clean fuel eligibility, geolocation data, utility providers, and census information. This dataset is ideal for analyzing the adoption of electric vehicles across regions, estimating electric range trends, or building classification and regression models related to vehicle types and eligibility.

This dataset shows the Battery Electric Vehicles and Plug-in Hybrid Electric Vehicles that are currently registered through Washington State Department of Licensing .

Metadata Created Date November 10, 2020
Metadata Updated Date May 24, 2024

This publication has been produced by Central Statistics and Research Branch (CSRB). It contains information on people’s awareness of and attitudes towards electric cars and the factors that would encourage or discourage them from buying an electric vehicle.

Source agency: Regional Development (Northern Ireland)

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: E-Cars research

Overview:

The Electric Vehicle Population Data dataset provides comprehensive insights into the distribution and characteristics of electric vehicles (EVs) across various regions.

Objective:

1. make EDA for understand dataset.
2. predict the type of electric vehicle based on its various characteristics

- The dataset is allowed to be modified during the analysis.

Details of the columns

#	Column Name	Description	Type
1	VIN (1-10)	The Vehicle Identification Number, which uniquely identifies the vehicle.	Categorical
2	County	The county where the vehicle is registered.	Categorical
3	City	The city where the vehicle is registered.	Categorical
4	State	The state where the vehicle is registered.	Categorical
5	Postal Code	The postal code associated with the vehicle's registration address.	Numerical (Float)
6	Model Year	The year the vehicle model was manufactured.	Numerical (Int)
7	Make	The manufacturer or brand of the vehicle (e.g., Tesla, Nissan).	Categorical
8	Model	The specific model of the vehicle (e.g., Model S, Leaf).	Categorical
9	E.v_Type	Classification of the electric vehicle, such as BEV (Battery Electric Vehicle) or PHEV (Plug-in Hybrid Electric Vehicle).	Categorical
10	CAFV	Indicates whether the vehicle qualifies as a Clean Alternative Fuel Vehicle under specific criteria.	Categorical
11	Electric Range	The maximum distance the vehicle can travel on electric power alone, typically measured in miles.	Numerical (Float)
12	Base MSRP	The Manufacturer's Suggested Retail Price before any options or additional features are added.	Numerical (Float)
13	Legislative District	The legislative district in which the vehicle is registered, possibly affecting incentives.	Numerical (Float)
14	DOL Vehicle ID	A unique identifier assigned by the state's Department of Licensing.	Numerical (Int)
15	Vehicle Location	Specific location details of where the vehicle is registered or primarily located.	Categorical
16	Electric Utility	The electric utility company that provides power to the vehicle's location.	Categorical
17	2020 Census Tract	The geographic area defined by the U.S. Census Bureau for demographic analysis.	Numerical (Float)

As of the first half of 2024, high fuel prices were the leading reason for survey respondents across the globe to consider the purchase of an electric vehicle. The global consumer price inflation contributed to respondents citing high fuel prices as a motivator to purchase EVs. Around ******* of participants highlighted environmental concerns as an EV purchase motivating factor.

BYD overtook Tesla as the best-selling electric vehicle brand in 2022 and maintained its position in the ranking through 2024. That year, BYD sold nearly *** million plug-in electric vehicles globally. Tesla and Wuling completed the top three brands that year, with Tesla reporting some **** million sales. BYD is also the leading manufacturer of electric vehicles in China, based on sales. European brands charge ahead in electric car sales With Europe-based brands Volkswagen, BMW, and Mercedes all impressively ranked in the plug-in electric vehicle (PEV) market, there is no doubt that Europe's incumbent automakers are ready to zoom past rivals when it comes to electric mobility. Volatility in the market has, however, come to light in 2020 following the coronavirus outbreak, as international auto sales contracted by around ** percent and plummeted to around ** million units. The global automotive chip shortage, accelerated by the pandemic, further contributed to challenges in the market throughout 2021 and 2022. The European electric vehicle market has been growing through these challenges, with plug-in electric vehicle sales representing nearly ** percent of the 2024 European passenger car sales. Fueling a green future With climate change still a hot topic of conversation across the globe, the adoption of fuel-cell electric vehicles (FCEV), which emit only water vapor into the environment, as well as hybrids, plug-in hybrids, and battery electric vehicles is becoming ever more enticing. Green transportation-related patent applications worldwide represented close to a quarter of all green patents filed in 2020.

The path to transportation decarbonization will rely heavily on electric vehicles (EVs) in the United States. EV diffusion forecasting tools are necessary to predict the impacts of EVs on local energy demand and environmental quality. Few EV adoption models operate at a fine spatial scale and those that do still rely on aggregated demographic information. This adoption model is one of the first attempts to employ a synthetic population to examine EV distribution at a fine spatial and demographic scale. Using a synthetic population at the Census-Tract-level, enriched with household fleet body types and home-charging access, we consider the effect of vehicle body type on EV spatial distribution and home-charging access in California. We examine two EV body type mixes in a high electrification scenario where 8 million EVs are distributed across 6 million households in California: a “Small Vehicles” scenario where 6 million EVs are passenger cars and 2 million EVs are trucks, sport utility ...

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,

Since the launch of the iZEV Program on May 1, 2019, Transport Canada has been producing statistics on consumer uptake under the program for the following variables: - Province/territory or all of Canada - Province/territory and postal code of the dealership each vehicle was purchased/leased from - Make and/or model (including model year) - Engine type (i.e., 100% battery electric versus plug-in hybrids - both over and under 50 km of electric range.) - Recipient type (i.e., individual or organization and purchase or lease) - A time period, including: * A specific month * Ranges of months (e.g., June 2020 to January 2021) * Calendar year (January 1 to December 31) * The Government of Canada’s fiscal year (April 1 to March 31) The current data provides iZEV monthly statistics. Revisions of archived data will be updated quarterly, these revisions are generally minor and are mainly due to approval of incentive requests that were incomplete when first submitted to Transport Canada. Most revisions are typically from the most recent three-month period. If you have any questions, please contact us at iZEV-iVZE@tc.gc.ca