German Aerospace Center (DLR)
Published August 6, 2025 | Version 1.0
Software Restricted

Consumer Habits based Approach to model Recharging and Grid INtegration (CHARGIN)

Authors/Creators

  • 1. EDMO icon German Aerospace Center (Berlin)

Contributors

  • 1. EDMO icon German Aerospace Center (Berlin)
  • 2. EDMO icon Brandenburg University of Technology (BTU)
  • 3. ROR icon RWTH Aachen University
  • 4. NOW GmbH

Description

Modelling the charging demand of electric vehicles

The CHARGIN model was developed at the Institute of Transport Research at the German Aerospace Center (DLR).  It enables the simulation of the time- and location-specific charging demands of electric vehicles (EVs) on a microscopic level and provides detailed information about occupied charging stations, the energy demand and the connected charging power of a vehicle fleet over the course of a week in hourly resolution. The results can also be used to derive flexibility potential for controlled charging. The simulation can consider time-varying charging prices for a charging decision in order to take into account scarcity signals from the electricity system.

 

Functionality

CHARGIN simulates charging demand based on mobility data, which can be derived, from surveys, such as National Travel Household Surveys, or from transport demand models. Scenario framework conditions, such as fleet composition and charging prices, are also taken into account for the modelling.
CHARGIN uses the input information to model the charging behaviour of each individual vehicle in the data set and uses this information to calculate the charging demand under the specified scenario conditions. Hourly-resolved profiles are calculated for the selected region over the course of a week, differentiated by charging location. The results include occupied charging stations, connected power, energy demand and flexibility potential.


Four overarching assumptions apply to the CHARGIN calculations:

  1. EVs will develop into a mass market.
  2. EV users will not change their travel behaviour significantly.
  3. EV users will prefer to charge where they already park.
  4. The energy system can meet the demand for electricity and energy at all times.

These assumptions are consistent with other studies and the expected user behaviour for a mass market of EVs.

 

Contact

Moritz Bergfeld
Institute of Transport Research
German Aerospace Center (DLR)
Moritz.Bergfeld@DLR.de

 

Files

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Additional details

Software

Programming language
Python
Development Status
Active

References

  • Anderson, J. E., Bergfeld, M., Nguyen, D. M., & Steck, F. (2022). Real-world charging behavior and preferences of electric vehicles users in Germany. International Journal of Sustainable Transportation, 17(9), 1032–1046. https://doi.org/10.1080/15568318.2022.2147041
  • Steck, Felix und Anderson, John Erik und Kuhnimhof, Tobias und Hoyer-Klick, Carsten (2019) Comprehensive transportation and energy analysis: A price sensitive, time-specific microsimulation of electric vehicles. In: CONFERENCE PROCEEDINGS 1: TRANSPORTATION RESEARCH BOARD. Transportation Research Board 98th Annual Meeting, 2019-01-13 - 2019-01-17, Washington, D.C., USA.
  • Wulff, N., Steck, F., Gils, H. C., Hoyer-Klick, C., van den Adel, B., & Anderson, J. E. (2020). Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling. Energies, 13(5), 1093. https://doi.org/10.3390/en13051093
  • Hoyer-Klick, Carsten and Anderson, John E. and Bergfeld, Moritz and Galich, Anton and Österle, Ines and Fahrner, Vera and Wulff, Niklas, Impact of Charging Behavior of Electric Vehicle Users on the Energy System. Available at SSRN: https://ssrn.com/abstract=4149594 or http://dx.doi.org/10.2139/ssrn.4149594