The urgent need to transform the German energy system toward climate neutrality has rarely been in such focus as it is today. The electrification of the industrial, transport and building sectors is being driven forward. The mobility sector plays a central role in this process and is currently transforming. Developments around electromobility are advancing and the impact on the existing and still to be expanded infrastructure must be investigated and assessed as best as possible. Distribution network operators are being confronted with new challenges as a result of the increasing number of electrical consumers. But also for component and car manufacturers, the charging behavior of electric vehicle users can be crucial. As a result, forecasts and modeling of the charging behavior of users of battery-powered electric vehicles are of paramount importance. To model this as realistic as possible, charging behavior at different charging stations, whether public, semi-public, private or workplace, must also be investigated. For this purpose, data sets of charging behavior from real-world charging events provide an important orientation and basis for studies to estimate the impact of charging behavior on current and future infrastructure. This article provides an overview of different data sets that capture the charging behavior of electric vehicle users.
The project “unIT-e² – Regulatory Sandboxes for Networked E-Mobility” investigates the holistic integration of electric mobility into the energy system. The content described here is being developed by FfE e.V. (Research Center for Energy Economics) and is funded by the German Federal Ministry for Economic Affairs and Climate (BMWK) under the code 01MV21UN11.
Private and public charging
When collecting data from real charging processes, a distinction is made between public, semi-public and private charging stations, depending on the accessibility of the location.
- Private charging stations are always located on private property and can only be used by the owner or a special group of people (e.g. employees). They are usually tied to the local power grid of the neighbouring building, with the owner providing the power. 
- Semi-public charging stations are also located on private property but have easier access. Users are usually customers at the same time. Since outside users can also charge their electric cars here, the station is often managed by a charge point operator (CPO). 
- Public charging stations are located on city or town-owned land, usually require authentication, and have a Charge Point Operator (CPO). 
Presentation of the data sets
Figure 1 first gives an overview of how many loading events were recorded per data set. Datasets that are not publicly available or were not available at the time the article was written are only listed here without the number of charging events.
Data for this dataset was collected in California between 2018 and 2021. Two different charging options were considered. A workplace charging opportunity with 54 charging points connected via two 150 kVA three-phase transformers and another semi-public charging opportunity with approximately 50 charging points. The location, start of charging, end of charging, and amount of energy charged were recorded.
Data collected at private charging points in the UK in 2017. Recorded energy amounts and other details of the charging event, such as plug-in time and duration.
Since 2012, data has been collected in Texas and California. Charging behavior at private charging points is being recorded for 50 – 100 battery electric vehicles. The dataset is open for research purposes and otherwise fee-based.
Collected in the Netherlands from 2012 to 2016, the dataset includes data from 1750 public charging stations with 2900 charging points. The three-phase charging stations are spread throughout the Netherlands. All have a maximum power of 12 kW. The considered charging points represent 16% of all public charging points in the Netherlands until 2015. Over the four years, approximately one million charging events were recorded in 15 s increments. During the data collection period, the charging stations were used by an estimated 30,000 electric vehicle drivers.
The charging time, idle time, plug-in time, charged energy, charging power, and geographic coordinates were recorded. It was observed that approximately 75% of plugged-in electric vehicles were fully charged.
Dataset of data from public, semi-public and workplace charging points collected between 2013 to 2020. In contrast to the evaluation of the dataset, the dataset itself is not public.
Integrated simulation model for residential load and mobility profiles, providing synthetic data. Generated by a Markov process, for an activity generator, a household load profile generator, and a mobility generator. The probabilities for the activities of the activity generator were determined by a time-use study. The model allows the creation of consistent load curves for different user groups and with regional differences.
Dataset of public charging stations in Boulder in the U.S. from 2018 to 2022. The amount of energy charged, charging time, fuel savings, and avoided CO2 emissions were tracked or calculated at all public, city-owned charging stations.
Data set collected from 2011 to 2022 that, like Colorado, records or calculates the amount of energy charged, charging duration, fuel savings, and avoided CO2 emissions.
The real-time dataset collects data from 2,500 charging stations with over 12,000 charging points in Norway, Finland, and Sweden. The data collected includes the types of the plugs, the charging capacity, and the geographic coordinates.
Captures data at six public, city-owned charging stations with 12 charging points between 2016 and 2017. Data is available on a cumulative basis for each month of the data collection period.
Data set of private charging in Ontario. The data set is not public.
The data set captures charging data from public 22 kW charging stations in the Paris Belib charging infrastructure from April to May 2017. The geographic location, the start and the end of charging, the energy charged, the charging power, the plug type, and the vehicle owner’s zip code are captured.
The availability of charging stations can be accessed in real time via an API.
This is a Gitlab repository.
Data set of public charging points in the city of Dundee in the UK from 2017 to 2018.
This data set collected data from public charging points in Perth and Kinros in the UK from 2016 to 2019.
Eco movement provides historical charging data against payment. Eco movement is a data provider for the European Commission, among others. A demo version can be requested.
This dataset collected data from private charging operations in Norway between 2018 and 2020. A total of 6,878 charging events were recorded, as well as the plug-in time, plug-out time, and charged energy. Synthetic charging power was generated in hourly resolution with assumed powers of 3.6 kW or 7.2 kW under the condition that the charging starts directly after plugging in the electric vehicle.
The data set collects data of public charging stations in Barcelona from 2019 and is divided into two data sets. The first captures information about the charging event, such as the charging point, the plug type, the start and end of charging, the charging duration, the charged energy, and the car manufacturer. The second dataset summarizes information about the charging point, such as the geographical location and the technical data of the charging point. A distinction is made between slow and fast charging. Between 3 kW and 7 kW the charging is classified as slow and between 43 kW and 55 kW as fast. The data set was created as part of the User-Chi project, which is being carried out in Berlin, Murcia, Budapest, Rome, Florence, Turku and Barcelona.
The data set collected at public charging stations in the Finnish city of Turku in 2019 includes the start and the end of charging, the charging duration, the charged energy, the cumulative energy provided, and information on whether it is 22 kW AC charging or 50 kW DC charging. The data set was created as part of the User-Chi project, which is being carried out in Berlin, Murcia, Budapest, Rome, Florence, Turku and Barcelona.
 Ostermann, A.; Müller, M.: Beitragsreihe Elektromobilität: Privates und öffentliches Laden, 2020, URL: https://www.ffe.de/veroeffentlichungen/beitragsreihe-elektromobilitaet-privates-und-oeffentliches-laden/, zuletz abgerufen am: 27.07.2022.