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 However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model.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).

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

-