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 However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model.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, other) String - type Event type defined by MATSim simulation String - link Link ID (link_id in File 5) String - distance_driven Cumulative driven distance in the simulation day Float km energy_1 Energy consumed while driving (-) or charging (+) (Strategy 1) Float kWh energy_2 Energy consumed while driving (-) or charging (+) (Strategy 2) Float kWh energy_3 Energy consumed while driving (-) or charging (+) (Strategy 3) Float kWh charger_1 Power rating of the charger (Strategy 1) Float kW charger_2 Power rating of the charger (Strategy 2) Float kW charger_3 Power rating of the charger (Strategy 3) Float kW soc_1 State of charge (0-1, Strategy 1) Float - soc_2 State of charge (0-1, Strategy 2) Float - soc_3 State of charge (0-1, Strategy 3) Float -

This research is funded by the Swedish Research Council Formas (Project Number 2018-01768). Sonia Yeh acknowledges the funding from H2020 European research programme (Grant agreement ID: 821124).