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zenodo44/100

Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes

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 However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model.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). <a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a> 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 <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> act_id </td> <td> Activity index of each agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> deso </td> <td> Zone code of Demographic statistical areas (DeSO)1 </td> <td> String </td> <td> - </td> </tr> <tr> <td> POINT_X </td> <td> Coordinate X of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> POINT_Y </td> <td> Coordinate Y of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> act_purpose </td> <td> Activity purpose (work, home, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> mode </td> <td> Transport mode to reach the activity location (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> dep_time </td> <td> Departure time in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> trav_time </td> <td> Travel time to reach the activity location </td> <td> String </td> <td> hour:minute:second </td> </tr> <tr> <td> trav_time_min </td> <td> Travel time in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> speed </td> <td> Travel speed to reach the activity location </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> distance </td> <td> Travel distance between the origin and the destination </td> <td> Float </td> <td> km </td> </tr> <tr> <td> act_start </td> <td> Start time of activity in minute (0-1439) </td> <td> Integer </td> <td> minute </td> </tr> <tr> <td> act_time </td> <td> Activity duration in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> act_end </td> <td> End time of activity in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> score </td> <td> Utility score of the simulation day given by MATSim </td> <td> Float </td> <td> - </td> </tr> </tbody> </table> 1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a>   (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. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> time </td> <td> Time in second in a simulation day (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation2 </td> <td> String </td> <td> - </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> link </td> <td> Nearest road link consistent with (5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> vehicle </td> <td> Vehicle ID identical to person </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table> 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 <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table>   (4) Car fleet composition File name: 4_car_fleet.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> income_class </td> <td> Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> </tbody> </table>   (5) Road network with slope information File name: 5_road_network_with_slope.shp (5 files in total) <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> length </td> <td> The length of road link </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> freespeed </td> <td> Free speed </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> capacity </td> <td> Number of vehicles </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> permlanes </td> <td> Number of lanes </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> oneway </td> <td> Whether the segment is one-way (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> modes </td> <td> Transport mode (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> link_id </td> <td> Link ID </td> <td> String </td> <td> - </td> </tr> <tr> <td> from_node </td> <td> Start node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> to_node </td> <td> End node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> count </td> <td> Aggregated traffic (number of cars travelled per day) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> slope </td> <td> Slope in percent from -6% to 6% </td> <td> Float </td> <td> - </td> </tr> <tr> <td> geometry </td> <td> LINESTRING (SWEREF99TM) </td> <td> geometry </td> <td> meter </td> </tr> </tbody> </table>   (6) Simulation scenarios specifying the parameter sets File name: 6_scenarios.txt <table> <tbody> <tr> <td> Parameter set (paraset) </td> <td> Strategy 1 </td> <td> Strategy 2 </td> <td> Strategy 3 </td> <td> Fast charging power (kW) </td> <td> Minimum parking time for charging (min) </td> <td> Intermediate charging power (kW) </td> </tr> <tr> <td> 0 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 1 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 2 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 3 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> </tbody> </table>   (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. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> <tr> <td> seq </td> <td> Sequence ID of time history by agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> time </td> <td> Time (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> purpose </td> <td> Valid for activities (home, work, school, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation </td> <td> String </td> <td> - </td> </tr> <tr> <td> link </td> <td> Link ID (link_id in File 5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> distance_driven </td> <td> Cumulative driven distance in the simulation day </td> <td> Float </td> <td> km </td> </tr> <tr> <td> energy_1 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 1) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_2 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 2) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_3 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 3) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> charger_1 </td> <td> Power rating of the charger (Strategy 1) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_2 </td> <td> Power rating of the charger (Strategy 2) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_3 </td> <td> Power rating of the charger (Strategy 3) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> soc_1 </td> <td> State of charge (0-1, Strategy 1) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_2 </td> <td> State of charge (0-1, Strategy 2) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_3 </td> <td> State of charge (0-1, Strategy 3) </td> <td> Float </td> <td> - </td> </tr> </tbody> </table>

opencc-by-4.0Feb 2023View details →

ClinicalTrials.gov24/100

EMI and CIED: Is High Power Charging of Battery Electric Car Safe?

ClinicalTrials.gov study NCT05361681. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

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