MobEV

MobEV is a mobility-based, geolocalized electric-vehicle (EV) charging-demand model for distribution-network studies. It turns weekly car-trip templates into charging sessions, battery state-of-charge traces, and demand profiles at a configured timestep.

MobEV requires Python 3.13 or newer:

python -m pip install mobev
# or, in a uv project
uv add mobev

How it works

1. Prepare trips: validate and expand weekday and weekend GeoPackages across the simulation dates.
2. Build the scenario: sample EV ownership and vehicle attributes, assign parking and chargers, and prepare tariffs and optional weather.
3. Simulate charging: process trips and parking chronologically to produce battery states, charging decisions, sessions, and active-slot power profiles.

Aggregation is a separate analysis step. Use it to sum charging power for the whole population or selected vehicles and locations, then plot or export it.

First run

Follow Getting started to create a minimal YAML configuration and two small synthetic trip GeoPackages. After creating those inputs, run:

from mobev import SimulationPipeline

pipeline = SimulationPipeline.from_yaml(
    config_path="example/config.yaml",
    trip_source_dir="example/trips",
)
result = pipeline.run()
print(result.charging_simulation.charging_sessions_df.head())

total = pipeline.aggregate(by="total")
print(total.aggregated_profiles_df.head())
total.plot(save_path="example/total.png")

The tutorial uses baseline consumption and a flat tariff. Temperature-based consumption requires enabling battery.consumption_mode: temperature and passing a separate hourly weather file. The repository's config/config.yaml is a study configuration that requires weather and tariff files.

Documentation

• Input formats
• Configuration reference
• Workflow, CLI, and scenario comparison
• Charging decision algorithm
• Result tables
• Aggregation and visualization
• API reference
• Changelog
• Contributing and building the docs