Entwicklung eines integrierten Kraftstoffverbrauchs- und Fahrtenkettenmodells des Straßengüterverkehrs am Beispiel schwerer Nutzfahrzeuge

Due to Germany’s ambitious climate protection goals, the increasing greenhouse gas (GHG) emissions from the transport sector – especially from heavy duty vehicles which are currently and in the foreseeable future almost exclusively powered by diesel engines – are increasingly at the centre of political and scientific discussion. Consequently, not only technical measures (i. e. higher energy efficiency and alternative drive technologies) but also changes in driving behaviour that can be implemented immediately, such as driving at reduced maximum velocity on motorways, should be taken into account and investigated by both policy makers and the actors directly involved in road freight transport (i. e. transport and logistics service providers). In order to analyse the real effects in a holistic and anticipatory manner on both the vehicle‘s physical-technical level and the transport service provider‘s logistic-economic level, this thesis links a physically-based fuel consumption model with a trip chain model. Firstly, the average values of both fuel consumption (in l/100 km) and velocity (in km/h) resulting from a reduction measure are determined by simulation experiments with a fuel consumption model based on MATLAB/Simulink. Secondly, these two variables are described as payload-dependent functions for each vehicle. A reduced maximum velocity is represented by correspondingly adapted driving cycles, which originate from HBEFA and are modified in this work with the help of an analytical procedure (programme). Each vehicle represents all vehicles in one of a total of 15 vehicle classes, which together account for around 91 % of diesel consumption and GHG emissions, respectively, of all heavy duty vehicles in Germany in 2010. Accordingly, the simulation results of one vehicle can be projected for all vehicles in each class with the given stock. Afterwards, the effects of a decreased average velocity to the temporal sequence of different trips, which have to be carried out by one vehicle within a year, are analysed with the trip chain model (discrete-event simulation model based on Excel-VBA). Due to constant frame conditions for both the vehicle (i. a. driving ban periods) and the driver (i. a. driving and rest periods), which are always fulfilled in the model, the results are not only increasing trip times, which behave inversely proportional to the velocity, but also some trips which have to be shifted to the next day or even the next week. The corresponding delay time is calculated and recorded with respect to the baseline situation (i. e. without reduced velocity). By recording and evaluating the increasing total personnel expenditure (i. e. working time on each vehicle), the corresponding costs can be offset against the total amount of saved fuel costs. Thereby, the economic efficiency of a reduction measure is finally determined on the basis of the anually increased or decreased total costs. The corresponding amount of potentially reduced GHG emissions from all heavy duty vehicles is specified, too.