SoC

This electric vehicle (EV) charging dataset includes the EV connection time, charging duration, energy consumption, and day number each corresponding respectively to connectionTime_decimal, chargingDuration, kWhDelivered, dayIndicator columns in the dataset for an EV charging parking lot. The data is generated using conditional tabular generative adversarial networks (CTGAN) and kernel density estimation (KDE) from the Caltech dataset to maintain a realistic load profile, accurately model EV owner behaviors, and preserve the relationship between the columns of the dataset. This dataset includes EV data for 29,600 days while the original Caltech dataset includes data for only 185 days. This data can be useful in training machine learning algorithms, specifically, reinforcement learning algorithms. The connection time range is 0-24. The unit for charging duration is hour and energy consumption is in kWh.

Citation

Gholizadeh, Nastaran (2024), “Electric Vehicle Charging Dataset”, Mendeley Data, V1, doi: 10.17632/5zrtmp7gwd.1

Acknowledgement

Foto von Maxim Hopman auf Unsplash

This data set is provided in support of a forthcoming paper: "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," [1]. These files include electricity demand profiles for 200 households randomly selected among the ones available in the 2009 RECS data set for the Midwest region of the United States. The profiles have been generated using the modeling proposed by Muratori et al. [2], [3], that produces realistic patterns of residential power consumption, validated using metered data, with a resolution of 10 minutes. Households vary in size and number of occupants and the profiles represent total electricity use, in watts. The files also include in-home plug-in electric vehicle recharging profiles for 348 vehicles associated with the 200 households assuming both Level 1 (1920 W) and Level 2 (6600 W) residential charging infrastructure. The vehicle recharging profiles have been generated using the modeling proposed by Muratori et al. [4], that produces real-world recharging demand profiles, with a resolution of 10 minutes. [1] M. Muratori, "Impact of uncoordinated plug-in electric vehicle charging on residential power demand." Forthcoming. [2] M. Muratori, M. C. Roberts, R. Sioshansi, V. Marano, and G. Rizzoni, "A highly resolved modeling technique to simulate residential power demand," Applied Energy, vol. 107, no. 0, pp. 465 - 473, 2013. https://doi.org/10.1016/j.apenergy.2013.02.057 [3] M. Muratori, V. Marano, R. Sioshansi, and G. Rizzoni, "Energy consumption of residential HVAC systems: a simple physically-based model," in 2012 IEEE Power and Energy Society General Meeting. San Diego, CA, USA: IEEE, 22-26 July 2012. https//doi.org/10.1109/PESGM.2012.6344950 [4] M. Muratori, M. J. Moran, E. Serra, and G. Rizzoni, "Highly-resolved modeling of personal transportation energy consumption in the United States," Energy, vol. 58, no. 0, pp. 168-177, 2013. https://doi.org/10.1016/j.energy.2013.02.055

This supplementary material includes data and code for the research described in the paper "Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling". 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.

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).

Output generated by the RAMP engine for use in the Sector-Coupled Euro-Calliope model. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope.

Cooking energy demand profiles (ramp-cooking-profiles): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the RAMP model [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**.

Electric vehicle plug-in profiles (ramp-ev-plugin-profiles): Profiles of the percentage of parked electric vehicles, stochastically generated using the RAMP-Mobility model [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*.

Electric vehicle energy consumption profiles (ramp-ev-consumption-profiles): Profiles of the electricity consumption of electric vehicles, stochastically generated using the RAMP-Mobility model [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*.

• AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE

** (*) + BGR, SRB

*** ALB, MKD, GRC, CYP, BIH, MNE, ISL

[1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. Energy 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097.

[2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. Applied Energy 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676.

The electric car market is experiencing explosive growth, projected to reach a market size of $122.87 billion in 2025 and maintain a robust Compound Annual Growth Rate (CAGR) of 24.2% from 2025 to 2033. This surge is driven by several key factors. Increasing concerns about climate change and air pollution are pushing governments worldwide to implement stricter emission regulations and incentivize electric vehicle (EV) adoption through subsidies and tax breaks. Simultaneously, technological advancements are leading to improved battery technology, resulting in longer driving ranges, faster charging times, and reduced costs. Consumer demand is rising steadily as EV prices become more competitive with traditional gasoline-powered vehicles, and the availability of charging infrastructure expands. The market is segmented by vehicle type (Plug-in Hybrid Electric Vehicles – PHEVs and Battery Electric Vehicles – BEVs) and application (home use and commercial use), with BEVs anticipated to dominate the market share due to their zero-emission profile and government support. Key players like BYD, Tesla, and Volkswagen are aggressively investing in research and development, expanding their product lines, and forging strategic partnerships to consolidate their market position. Regional variations exist, with North America, Europe, and Asia Pacific representing the largest markets, driven by differing levels of government support, consumer awareness, and charging infrastructure development. The market faces challenges such as the high initial cost of EVs, range anxiety, and the need for further development of charging infrastructure, particularly in less developed regions. However, ongoing technological advancements and supportive government policies are mitigating these restraints. The competitive landscape is highly dynamic, with established automakers and new entrants vying for market share. Companies like BYD, Tesla, and Volkswagen are leading the charge, but other prominent players like GM, BMW, and Toyota are also making significant investments in the EV market. The success of individual companies will depend on their ability to innovate, effectively manage supply chains, and adapt to the rapidly evolving technological landscape. Future growth will be significantly influenced by the successful integration of advanced battery technologies, improvements in charging infrastructure, and the continuous development of charging solutions suitable for both individual and commercial use cases. The ongoing shift towards sustainable transportation solutions, coupled with increasing consumer demand, ensures that the electric car market will remain a dynamic and lucrative sector in the coming years.

Electric vehicles amounted to around 16.7 percent of global passenger car sales in 2023, which was a rise of around 3.1 percentage points year-over-year. Electric vehicle sales have rapidly increased since 2017, when they rose above one percent of the market, and have particularly accelerated since 2020. Many consumers started looking for more sustainable transportation methods amid the COVID-19 pandemic due to increased environmental consciousness. This contributed to the EV market expansion worldwide. A market driven by innovation Various factors contribute to the rapid growth of the electric vehicle market, including consumer perception, governmental targets, and investments in technological innovation. Regional institutions and national governments are committing to policies supporting electric vehicle adoption worldwide, with around 97 percent of the light-duty vehicle market comprising countries with these policies. Governmental spending on electric cars reached around 45 billion current U.S. dollars in 2022, the steepest increase recorded in the past five years, and global automakers are also allocating part of their revenue toward research and development expenses. Challenges and opportunities for EV charging Electric vehicle charging was the second technology type receiving the most early and growth-stage venture capital investments in 2023, above electric vars and electric two-wheelers. In 2023, there were around 11 electric vehicles per charging point worldwide, and access to this infrastructure was unequal, with China boasting the largest electric vehicle supply equipment network. Slow chargers, typically alternating current, were also the most common charging type, creating opportunities for the development of fast charging across the globe.

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.

This dataset represents a complete European energy community based on actual data. In this scenario, a community of 250 households was built using real energy consumption and solar generation data obtained in homes throughout Europe. In total, 200 community members were assigned solar generation, while 150 were assigned a battery storage system. From the acquired sample, new profiles were created and randomly assigned to each end-user while also receiving two electric cars with information on their capacity, state-of-charge, and usage. Furthermore, it is provided the electric vehicle chargers’ information on their location, type, and cost of operation.

Version 1.5 update: on the Sheet EVs, lines 29 (Capacity kW), 30 (Charge kW), and 31 (Discharge kW) were updated to the correct values.

This work has been published in Elsevier's Data in Brief journal: Ricardo Faia, Calvin Goncalves, Luis Gomes, Zita Vale Dataset of an energy community with prosumer consumption, photovoltaic generation, battery storage, and electric vehicles Data in Brief, 2023, 109218, ISSN 2352-3409 https://doi.org/10.1016/j.dib.2023.109218 (https://www.sciencedirect.com/science/article/pii/S2352340923003372)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.

Reference data used to create this dataset:

Filtered energy profiles and renewable energy production profiles: https://zenodo.org/record/6778401

Battery storage systems and electric vehicles: https://zenodo.org/record/4737293

The global Electric Vehicle (EV) Market size is expected to reach USD 1,210.82 Billion in 2032 registering a CAGR of 18.3% Discover the latest trends and analysis on the Electric Vehicle (EV) Market. Our report provides a comprehensive overview of the industry, including key players, market share, g...

Charging data are collected from one of three sources, each with varying levels of additional information. These sources, in approximate order from most to least additional information, are: • The electric vehicle supply equipment (charger) • Onboard the vehicle itself • From a utility submeter. Many chargers provide software that allows for the collection and reporting of charging session data. If unavailable, data may be recorded by the charging vehicle’s onboard systems. If neither of these options is available, data can be acquired from utility submeters that simply track the energy flowing to one or more chargers. Data collected directly from the electric vehicle supply equipment (EVSE) are typically the most accurate and highest frequency. However, it is not always possible to discern which exact vehicle is being charged during any one session. EVSE-side data can be identified where a single charger ID but a range of vehicle IDs are present (e.g., CH001, EV001-EV005). Data collected from the vehicle’s onboard systems usually does not provide information on which exact charger is being used. Vehicle-side data can be identified where a single Vehicle ID but a range of Charger IDs are present (e.g., EV001, CH001-CH005). Data collected from utility submeters provide no information on which specific vehicle is charging or which specific charger is in use. Submeter data can be identified where multiple Vehicle IDs and multiple Charger IDs are present, but only a single Fleet ID is present (e.g., EV001-EV005, CH001-CH005, Fleet01). The Charge Data Daily/Session Dictionaries contains definitions for each available parameter collected as part of an individual charging session, aggregated at either a daily or session level. The parameters available will vary between vehicles and chargers. The Charger Attributes table contains specific charger characteristics, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. The Charger Attributes Data Dictionary contains definitions for each available parameter collected on the physical and operational characteristics of the charging hardware itself. The Vehicle Attributes Data Dictionary contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The Vehicle Attributes table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables, and in cases where charging data are supplied, links a vehicle with the charger(s) that supplied it power. The Charging Data tables contain the data from each charger’s operations, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

This dataset represents a complete European energy community based on actual data. In this scenario, a community of 250 households was built using real energy consumption and solar generation data obtained in homes throughout Europe. In total, 200 community members were assigned solar generation, while 150 were assigned a battery storage system. From the acquired sample, new profiles were created and randomly assigned to each end-user while also receiving two electric cars with information on their capacity, state-of-charge, and usage. Furthermore, it is provided the electric vehicle chargers’ information on their location, type, and cost of operation.

This work has been published in Elsevier's Data in Brief journal: Ricardo Faia, Calvin Goncalves, Luis Gomes, Zita Vale Dataset of an energy community with prosumer consumption, photovoltaic generation, battery storage, and electric vehicles Data in Brief, 2023, 109218, ISSN 2352-3409 https://doi.org/10.1016/j.dib.2023.109218 (https://www.sciencedirect.com/science/article/pii/S2352340923003372)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.

Reference data used to create this dataset:

Filtered energy profiles and renewable energy production profiles: https://zenodo.org/record/6778401

Battery storage systems and electric vehicles: https://zenodo.org/record/4737293

This dataset provides a detailed overview of the electric vehicle (EV) market in India from 2001 to 2024. It includes monthly sales data, sales data categorized by manufacturer, and vehicle class-wise sales data for different manufacturers. This rich dataset is ideal for market analysis, trend forecasting, and research on the adoption and growth of electric vehicles in India.

Updated Files Included

EV Maker by Place

List of popular EV Makers and their location of Manufacturing Plant

Operational PC

Total Operational Public Charging Station for EV available in each state

Vehicle Class

Total vehicles (includes electric and all other fuels) registered (manufactured) by category from 2001 - Aug 2024

ev_cat_01-24

Total electric vehicles manufactured from 2001 - Aug 2024 and vehicle category

ev_sales_by_makers_and_cat_15-24

Total electric vehicles manufactured by makers from 2015 - Aug 2024 with the vehicle class

Potential Uses

• Market Analysis: Understand the growth trends and market share of different manufacturers in the Indian EV market.
• Forecasting: Predict future sales trends based on historical data.
• Research: Study the adoption patterns and the impact of various factors on the EV market in India.

Acknowledgments

This dataset was compiled and web-scrapped from Vahaan4 Dashboard

Note - The data for name of Manufacturers is only available from 2015

📝 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 dataset contains the usage data of a single electric car collected in as part of the EVE study (Enquête des Vehicles Electrique) run by the Observatoire du Transition Energétique Grenoble (OTE-UGA). This dataset includes the following variables for a single Renault ZOE 2014 Q90: - Speed, distance covered, and other drivetrain data variables; - State of charge, State of health and other battery characteristics; as well as - external temperature variables. The Renault ZOE 2014 Q90 has a battery capacity of 22 KWh and a maximum speed of 135 KM/h. More information about on the specifications can be found here If you find this dataset useful or have any questions, please feel free to comment on the discussion dedicated to this dataset on the OTE forum . The electric car is used for personal use exclusively including occasional transit to work but mostly for personal errands and trips. The dataset was collected using a CanZE app and a generic car lighter dongle. The dataset spans three years from October 2020 to October 2023. A simple Python notebook that visualises the datasets can be found here. More complex use-cases for the datasets can be found in the following links: - Comparison of the carbon footprint of driving across countries: link - Feedback indicators of electric car charging behaviours: link There is also more information on the collection process and other potential uses in the data paper here. Please don't hesitate to contact the authors if you have any further questions about the dataset.

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,

The datasets contain percentile profiles for residential EV charging. The profiles are created based on a large dataset of around 3500 residential EV chargers owned by one of the largest charging point operators in Denmark. For each charger, data for the entire year of 2023 were used. Various percentile profiles are provided for different aggregation sizes (1-200 EVCs), seasons (summer and winter), days of the week (workday and weekend) as well as charger types, namely 3.7 kW (1-phase, 16 A), 7.8 kW (2-phase, 16 A) and 11 kW (3-phase, 16 A). Moreover, different group percentiles are provided indicated by the file name q_group0_95 uses the 95th-percentile. More information about the method and data can be found in an article submitted to CIRED Workshop 2024 with the title "Danish Electric Vehicle Charging Profiles for Distribution Grid Planning and Design".

The European high-performance electric car market is experiencing robust growth, driven by increasing consumer demand for sustainable and powerful vehicles. The market, valued at approximately €5 billion in 2025 (estimated based on provided CAGR and market size data), is projected to expand significantly over the forecast period (2025-2033), with a compound annual growth rate (CAGR) of 24.12%. This surge is fueled by several key factors: stringent government regulations promoting electric vehicle adoption, advancements in battery technology leading to increased range and performance, and growing consumer awareness of environmental concerns. Furthermore, the continuous innovation by major automotive players like Tesla, Volkswagen, and BMW, introducing high-performance electric models with impressive acceleration and sophisticated features, further stimulates market growth. The segment encompassing passenger cars currently dominates the market share, but the commercial vehicle segment is expected to witness substantial growth in the coming years, driven by the increasing demand for electric fleets in urban areas and logistics sectors. The strong presence of established automotive manufacturers and the emergence of innovative electric vehicle startups contribute to the market's dynamic and competitive landscape. The market's growth, however, faces certain restraints. High initial purchase prices of high-performance electric vehicles remain a barrier to entry for many consumers. Concerns surrounding charging infrastructure availability and range anxiety also influence consumer purchasing decisions. Overcoming these challenges requires concerted efforts from governments and the automotive industry to promote affordable electric vehicle options, expand charging networks across Europe, and build consumer confidence in electric vehicle technology. Despite these restraints, the long-term outlook for the European high-performance electric car market remains positive, with continued growth projected throughout the forecast period, driven by technological advancements, favorable government policies, and shifting consumer preferences. The UK, Germany, France, and Norway are expected to be key market contributors due to their established EV infrastructure and supportive government initiatives. Here's a report description incorporating the provided information and aiming for high search engine visibility. Note that creating actual hyperlinks requires knowing the exact URLs of the company websites, which I don't have access to. I've included placeholder text where links would normally go. Europe High Performance Electric Car Market: A Comprehensive Analysis (2019-2033) This comprehensive report provides a detailed analysis of the burgeoning Europe high-performance electric car market, covering the period from 2019 to 2033. With a base year of 2025 and a forecast period extending to 2033, this in-depth study offers valuable insights into market dynamics, trends, and future growth potential. The report analyzes key market segments including Battery Electric Vehicles (BEVs), Plug-in Hybrid Electric Vehicles (PHEVs), passenger cars, and commercial vehicles, offering a granular view of this rapidly evolving sector. The study covers several leading manufacturers such as Tesla, Volkswagen, BMW, and many others, examining their market share and strategies. Recent developments include: June 2023: Mercedes-Benz revealed the AMG EQE 53 4MATIC+ SUV. Mercedes-AMG's latest model stands out as the most adaptable electric vehicle in their lineup, combining a customizable cabin with a performance-oriented drive concept., May 2023: Aston Martin announced a collaboration with Bowers & Wilkins as its audio partner to provide a new surround sound system in its vehicles. They will concentrate on creating an optional surround sound system upgrade, as well as technical innovation and great performance. Aston Martin will use a Bowers & Wilkins audio system in future vehicles in the coming years., August 2022: In anticipation of the IAA Transportation 2022, ZF Friedrichshafen AG (ZF) stated that its Commercial Vehicle Solutions (CVS) division had exhibited the most modern mobility innovations. The all-electric powertrain combines cutting-edge control technologies to reimagine the dynamic, elegant, and precise mix that marks BMW M automobiles as high-performance sports cars., July 2022: Ford presented the new F-150 Raptor R, which will be powered by a 5.2 l V8 engine producing 700 HP. Its launch is the consequence of consumer demand for a Raptor with a V8 engine. Ford's new F-150 Raptor R includes characteristics of previous versions with a performance increase.. Key drivers for this market are: Increasing Demand of Luxury Vehicles is Expected to Drive the Market. Potential restraints include: High Cost of the Vehicle may Hinder the Market Growth. Notable trends are: Increasing Demand of Luxury Vehicles is Expected to Drive the Market.

Electric vehicle charging infrastructure historical and current session data from the present day to 2019 covering the continental US and Europe. Charging stations are logged by the minute to see the individual sessions over a stations' life. Our dataset contains sessions for approximately 90,000 stations.

Our data is unique in that it is aggregated from a wide range of sources, and internally enhanced, giving us the ability to provide a consistent set of validated and accurate data. Users can use this data to deeply understand charging station utilization.

Use cases for this data include (but are not limited to): Market research, usage prediction for electric utility companies, charger usage prediction for up and coming locations, and more. We can also help municipalities with identifying supply and demand gaps.

Learn more at evseinsights.com.