Range determination in road networks for battery-powered vehicles

Approximately 28.2% of CO2 emissions in Austria originate from the transport sector - 40% of which come from passenger transport alone. Battery-electric vehicles (BEVs), which have become increasingly common on Austria’s roads in recent years, represent an attractive alternative to conventional combustion engines. In addition to the still high purchase costs and the sometimes insufficient availability of e-charging stations, the issue of the limited range of electric vehicles is a major obstacle for many potential buyers. In order to counteract this so-called ”range anxiety”, AVL List GmbH in Graz is currently working on a routing application which, in contrast to conventional products, will calculate not only the shortest and fastest route but also the most energy-efficient route and thus the route option with the highest range for the electric vehicle. In the course of this diploma thesis, a MATLAB Simulink model was designed to determine the actual energy requirements for a given route or a set of routes in order to determine the most energy efficient one. In addition to the actual vehicle model, the AVL routing application was adapted, a Python program for data preprocessing and filtering was developed and extensive literature research on the topics of electromobility, driving dynamics, routing algorithms and electric machines was carried out - corresponding summaries can be found in the following chapters and in the appendix of this thesis. The main objective of this work, in addition to creating the vehicle model and the necessary peripheral systems, was to find out which influencing parameters should be taken into account in a future routing application in order to enable the most accurate possible range estimation for the specific vehicle on the one hand and to realise a time interval for the route calculation that is still acceptable for the user on the other. In addition to the three-dimensional route information, weather data, road conditions, cornering, driver behaviour and the influence of various auxiliary consumers depending on the time of year and time of day were also taken into account. These tests were carried out with two vehicles - the exact consumption data, vehicle specifications as well as the efficiency of the electric motor, the various powertrain components and various auxiliary consumers were kindly provided by AVL. A total of 12 influencing parameters were varied as part of the sensitivity analysis. As a result, a range variation of 414 km to 880 km was determined for the Tesla Model 3 LR and - with an annual vehicle utilisation of 10,000 km - an estimate of the annual energy costs in the range of € 470 to € 1013.