Main module for preparation of model data (static and timeseries) for heavy duty transport.

Contents of this module

  • Creation of DB tables
  • Download and preprocessing of vehicle registration data from BAST
  • Calculation of hydrogen demand based on a Voronoi distribution of counted truck traffic among NUTS 3 regions.
  • Writing results to DB
  • Mapping demand to H2 buses and writing to DB
class HeavyDutyTransport(dependencies)[source]

Bases: egon.data.datasets.Dataset

Class for preparation of static and timeseries data for heavy duty transport.

Resulting tables


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

Scenarios and variations

Assumptions can be changed within the datasets.yml.

In the context of the eGon project, it is assumed that e-trucks will be completely hydrogen-powered and in both scenarios the hydrogen consumption is assumed to be 6.68 kg H2 per 100 km with an additional supply chain leakage rate of 0.5 %.

### Scenario NEP C 2035

The ramp-up figures are taken from Scenario C 2035 Grid Development Plan 2021-2035. According to this, 100,000 e-trucks are expected in Germany in 2035, each covering an average of 100,000 km per year. In total this means 10 billion km.

### Scenario eGon100RE

In the case of the eGon100RE scenario it is assumed that the HGV traffic is completely hydrogen-powered. The total freight traffic with 40 Billion km is taken from the BMWK Langfristszenarien GHG-emission free scenarios (SNF > 12 t zGG).


Using a Voronoi interpolation, the censuses of the BASt data is distributed according to the area fractions of the Voronoi fields within each mv grid or any other geometries like NUTS-3.

name = 'HeavyDutyTransport'
version = '0.0.2'

Drops existing demand.egon_heavy_duty_transport_voronoi is extended table and creates new one.


Downloads BAST data.

The data is downloaded to file specified in datasets.yml in section mobility_hgv/original_data/sources/BAST/file.