On Battery Electric Vehicles: Driving Patterns, Multi-Car Households and Infrastructure

Licentiate thesis, 2016

The transportation sector is responsible for a quarter of all greenhouse gas emissions in Europe. Though the transport system may be difficult to change into a less polluting system, electric vehicles may be a possible approach. For personal use, there are today different models of battery electric vehicles, and plug-in hybrid electric vehicles available. Though battery electric vehicles have the bigger potential for reducing emissions, they also have the biggest hurdle in terms of range limitation and investment cost. In this thesis we have assessed battery electric vehicles performance as replacements for conventional cars using GPS-based driving pattern analysis. We have found that for common battery electric vehicle ranges of 120 km, a noteworthy adaptation is required for the average user. However, it is possible to specifically adopt battery electric vehicles within multi-car households to significantly reduce the need for adaptation. When applying a cost, resembling that of a rental car, for the days that the battery electric vehicle cannot fulfill the driving need of the user, close to 14% of the second cars in multi-car households would have a lower total cost of ownership as a battery electric vehicle compared to a conventional car in Sweden. We have also assessed the degree of adaptation in a small set of Swedish two-car households who adopted a battery electric vehicle for 3-4 months. Though the data set is small, it displays a large degree of heterogeneity in behavior, with some households increasing the use of the battery electric vehicle compared to the replaced car, while some decrease it, and others make virtually no change in travel behavior. Overall, we do not see a large increase in driving of the battery electric vehicle compared to the replaced car. We have also done methodological development by analyzing the effect of modelling driving data with three probability distributions. Contrary to earlier literature we find that the Weibull and Log-Normal distributions overall fit driving data better than the Gamma distribution. Additionally, for electric vehicles there are specific applications that are interesting, for battery electric vehicles: estimating the frequency of long-distance driving above the range limitation; and for plug-in hybrids, estimating the frequency of short-distance driving that may give rise to a high electric drive fraction. With regards to these applications, we find that the distributions systematically give different estimates, and that a researcher may choose distribution according to the chosen research question. Finally, we have analyzed the usage of fast charging infra-structure in Sweden to support assumptions made for a queueing model of charging infra-structure usage developed at Fraunhofer ISI, in Karlsruhe, Germany.