The ALADIN model is an agent-based simulation of alternative fuel
vehicles purchase decision. It uses driving data from several thousand
individual vehicles. The core of the model is to calculate the total cost of
ownership for different drivetrains (e. g. gasoline, diesel, BEV, PHEV for
passenger cars) based on large data sets for individual user driving behavior
[MOP 2010, Fraunhofer ISI 2014, KiD 2010, Truckscout 2016] and to determine the
utility maximizing driving option under various restrictions including infrastructure
or the limited model availability of new drivetrain technologies. Thereof, the
share of each drivetrain technology is calculated, reduced due to
infrastructure and limited vehicle availability and then considered as market
share for the year under consideration [Plötz et al. 2014, Wietschel et al.
The ALADIN model is available for market diffusion of alternative fuel
vehicles in the passenger car and light to heavy duty vehicle market both for
Germany and Europe.
The initial model version was developed for the German National Platform Electromobility (NPE) and focused on plug-in electric vehicles in Germany. Electric driving is simulated for about 7,000 conventional vehicles driving profiles from [MOP 2010, Fraunhofer ISI 2014] to determine the feasibility with a battery electric vehicle (BEV) and the electric driving share of a plug-in hybrid electric vehicle (PHEV). Thereafter, the utility maximizing option for each vehicle is determined while utility contains the total cost of ownership, the cost for a primary charging point as well as a user-specific willingness to pay more for plug-in electric vehicles. The share of BEVs and PHEVs is then taken as market share for new vehicle registrations. In Gnann (2015), the German model was extended to also contain a comprehensive evaluation of public charging infrastructure while fuel-cell electric vehicles were added in other projects.
In 2015, we
extended the model to include the market diffusion for alternative drive trains
for heavy-duty vehicles in Germany until 2030. The idea is similar, yet for
heavy duty vehicles the purchase decision is only based on cost. Since there is
no dominating alternative drive train technology at the moment, we compare the
market diffusion for catenary hybrid vehicles, full electric vehicles, natural
gas vehicles and plug-in electric vehicles. Here, we also perform deeper
analyses for the infrastructure usage and the impact on the energy system of
catenary hybrid electric vehicles. The extension to Europe comes with some
sensible simplifications, since not all data is available in the same level of
detail. For both PEVs as passenger cars and catenary highway trucks, we
transform the German market diffusion of other European markets considering the
country-specific differences in energy prices, current state of AFV diffusion
and development of charging infrastructure setup. The resulting market
diffusion is also used in energy systems models for further analyses.
Fraunhofer ISI 2014. REM2030 Driving Profiles Database V2014-07. Fraunhofer Institute of Systems and Innovation Research ISI, Karlsruhe, Germany.
Gnann, T. (2015): Market diffusion of plug-in electric vehicles and their charging infrastructure. Fraunhofer-Verlag Stuttgart
KiD 2010. WVI, IVT, DLR und KBA (2010): Motor vehicles in Germany 2010 . WVI Prof. Dr. Wermuth Verkehrsforschung und Infrastrukturplanung GmbH, Braunschweig, IVT Institut für angewandte Verkehrs- und Tourismusforschung e.V., Heilbronn, DLR Deutsches Zent-rum für Luft- und Raumfahrt – Institut für Verkehrsforschung, Berlin, KBA Kraftfahrt-Bundesamt, Flensburg
MOP 2010. German mobility panel 1994–2010. Tech. Rep., Project processing by Institute for Transport studies of the University of Karlsruhe (TH) (www.clearingstelle-verkehr.de).
Plötz, P; Gnann, T.; Wietschel, M. (2014): Modelling market diffusion of electric vehicles with real world driving data — Part I: Model structure and validation Elsevier, Ecological Economics Vol 107, Nov 2014, pages 411-421
Truckscout 2016: Sales platform for used utility vehicles. Online at http://www.truckscout24.de, last checked at 13.02.2017
Wietschel, M.; Gnann, T.; Kühn, A.; Plötz, P.; Moll, C.; Speth, D.; Buch, J.; Boßmann, T.; Stütz, S.; Schellert, M.; Rüdiger, D.; Balz, W.; Frik, H.; Waßmuth, V.; Paufler-Mann, D.; Rödl, A.; Schade, W.; Mader, S. (2017): Feasibility study on hybrid overhead trucks. Study within the framework of the scientific advice for the Federal Ministry of Transport and Digital Infrastructure on mobility and fuel strategy. Karlsruhe: Fraunhofer ISI