@article{ref1,
title="Running on empty - Users' charging behavior of electric vehicles versus traditional refueling",
journal="Transportation research part F: traffic psychology and behaviour",
year="2018",
author="Philipsen, Ralf and Brell, Teresa and Brost, Waldemar and Eickels, Teresa and Ziefle, Martina",
volume="59",
number="",
pages="475-492",
abstract="The demand-oriented establishment of a charging infrastructure is crucial for the deep market penetration of electric vehicles. In order to identify specific optimal locations for charging stations, a deeper understanding of the users' charging behavior is required. For reliable simulations and forecasts, it is not only necessary to take the charging behavior of the current early-adopter group into account, but also to compare it with the current refueling behavior of those, who could switch to electric vehicles in the near and medium-term future. The study follows a two-stage research approach. First of all, qualitative interviews (N=24) were conducted to identify refueling behavior in terms of behavioral patterns, refueling motives and conditions. The second step involves a quantitative comparison of the refueling and charging behavior with regard to conditions, frequencies and critical levels. A large-scale questionnaire study (N=1021) with car drivers from Germany was carried out for this purpose. The results showed that the conditions for a refueling or charging decision only differ partially. While financial aspects play a minor role for e-vehicle users, for internal combustion engine users planning and habit are less important. There is no difference between the two groups regarding range-relevant factors. In particular, the filling level perceived as critical is identical for fuel tanks and batteries. In terms of behavior patterns, e-vehicle users tend to charge consumed quantities in a timely manner, while users of vehicles with combustion engines often run on empty and then refill the tank completely. Only a few predictors for both behaviors could be identified. While socio-demographic factors hardly play a role, socio-economy and vehicle utilization are the biggest predictors. The results are incorporated into the modeling of potential users when planning new charging infrastructure.<p /> <p>Language: en</p>",
language="en",
issn="1369-8478",
doi="10.1016/j.trf.2018.09.024",
url="http://dx.doi.org/10.1016/j.trf.2018.09.024"
}