Assessing the Effects of a Growing Electric Vehicle Fleet Using a Microscopic Travel Demand Model

 

Christine Weiss*, Michael Heilig**, Nicolai Mallig***, Bastian Chlond****, Thomas Franke*****, Tina Schneidereit****** and Peter Vortisch*******

*Institute for Transport Studies (IfV), Karlsruhe Institute of Technology (KIT), Kaiserstrasse 12, 76131 Karlsruhe, Germany
T: +4972160847737
E: christine.weiss@kit.edu

**Institute for Transport Studies (IfV), Karlsruhe Institute of Technology (KIT), Kaiserstrasse 12, 76131 Karlsruhe, Germany
E: m.heilig@kit.edu

***Institute for Transport Studies (IfV), Karlsruhe Institute of Technology (KIT), Kaiserstrasse 12, 76131 Karlsruhe, Germany
E: nicolai.mallig@kit.edu

****Institute for Transport Studies (IfV), Karlsruhe Institute of Technology (KIT), Kaiserstrasse 12, 76131 Karlsruhe, Germany
E: bastian.chlond@kit.edu

*****Institute for Multimedia and Interactive Systems, University of Luebeck, Ratzeburger Allee 160, 23562 Luebeck, Germany
E: franke@imis.uni-luebeck.de

******Cognitive and Engineering Psychology, Technical University Chemnitz, Wilhelm-Raabe-Strasse 43 , 09120 Chemnitz , Germany
E: tina.schneidereit@psychologie.tu-chemnitz.de

*******Institute for Transport Studies (IfV), Karlsruhe Institute of Technology (KIT), Kaiserstrasse 12, 76131 Karlsruhe, Germany
E: peter.vortisch@kit.edu

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Abstract

The German government seeks to increase the number of electric vehicles (EV) in the German car fleet to one million by 2020. Since some characteristics of EVs differ from conventional cars, there is an increasing need to assess the various impacts of a growing EV fleet. In this work, we have focused on possible effects related to the field of transport. We identified three important aspects and evaluated them over a period of one week using the microscopic travel demand model mobiTopp. First, we modelled the potential EV user groups of the near future by developing an EV user model; this model considers both interest in EVs and suitability for EV usage. Second, we simulated the travel behaviour of EV users; we used an EV usage model to consider the restrictions of EVs in choice decisions and also compared the usage behaviour of EV and conventional cars users. Third, we analysed the power consumption of the simulated EVs and evaluated the load peaks based on the simulated travel patterns. Our results indicate that a growing EV fleet implies a more heterogeneous distribution of EVs among car owners. They also indicate that the trip chain length of battery electric vehicles (BEVs) is much lower than that of extended range electric vehicles (EREVs) and conventional cars on average.

Keywords: electric vehicles, vehicle ownership, car usage, agent based model, travel demand model