Methodology

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. 2017].

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.  We also offer a solution for overseas reagions (US, China and India) and on German NUTS 3 level for passenger cars, heavy-duty vehicles and inland navigation.

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.