motorized_individual_travel

Motorized Individual Travel (MIT)

Main module for preparation of model data (static and timeseries) for motorized individual travel.

Contents of this module * Creation of DB tables * Download and preprocessing of vehicle registration data from KBA and BMVI * Calculate number of electric vehicles and allocate on different spatial

levels. See egon.data.metadata
  • Extract and write pre-generated trips to DB

Configuration

The config of this dataset can be found in datasets.yml in section emobility_mit.

Scenarios and variations

  • Scenario overview
  • Change scenario variation for 2050: adjust in

emobility_mit->scenario->variation->eGon100RE

Trip data

The electric vehicles’ trip data for each scenario have been generated using simBEV. The methodical background is given in its documentation.

6 different vehicle types are used: * Battery Electric Vehicle (BEV): mini, medium, luxury * Plug-in Hybrid Electric Vehicle (PHEV): mini, medium, luxury

EV types
Tecnnology Size Max. charging capacity slow [kW]      
Max. charging capacity fast [kW] Battery capacity [kWh]        
Energy consumption [kWh/km]          
BEV mini 11 120 60 0.1397
BEV medium 22 350 90 0.1746
BEV luxury 50 350 110 0.2096
PHEV mini 3.7 40 14 0.1425
PHEV medium 11 40 20 0.1782
PHEV luxury 11 120 30 0.2138

The complete tech data and assumptions of the run can be found in the metadata: <WORKING_DIRECTORY>/data_bundle_egon_data/emobility/mit_trip_data/<SCENARIO>/ metadata_simbev_run.json.efficiency_fixed

  • explain scenario parameters
  • run params (all in meta file?)

EV allocation

The EVs per registration district (Zulassungsbezirk) is taken from KBA’s vehicle registration data. The numbers per EV type (BEV and PHEV)

  • RegioStaR7
  • scenario parameters: shares

Further notes

  • Sanity checks

Model paametrization

Example queries

class MotorizedIndividualTravel(dependencies)[source]

Bases: egon.data.datasets.Dataset

adapt_numpy_float64(numpy_float64)[source]
adapt_numpy_int64(numpy_int64)[source]
create_tables()[source]

Create tables for electric vehicles

Returns:None
download_and_preprocess()[source]

Downloads and preprocesses data from KBA and BMVI

Returns:
  • pandas.DataFrame – Vehicle registration data for registration district
  • pandas.DataFrame – RegioStaR7 data
extract_trip_file()[source]

Extract trip file from data bundle

write_evs_trips_to_db()[source]

Write EVs and trips generated by simBEV from data bundle to database table

write_metadata_to_db()[source]

Write used SimBEV metadata per scenario to database.