Electrification of Private Mobility: Driving Patterns, Multi-Car Households and Infrastructure

Abstract: Electrification of personal vehicles has the potential to significantly reduce carbon emissions. However, a large-scale transition to electric vehicles may be difficult as there are many individuals who collectively need to transition to this technology. Thus, it is important to understand car users needs, and to what extent a fully battery electric vehicle (BEV) fulfill these needs. In particular, batteries have been expensive and charging infrastructure scarce, thus creating a trade-off between the price of the car, and its driving range. We use several GPS-measured driving data sets, interview data, and charging infrastructure data to analyse potential BEV adoption in multi-car households. Furthermore, we develop methods with regards to driving data modelling and analysis. We also estimate the size of a future charging infrastructure network. We find that for short-range BEVs (120 km), a noteworthy adaptation is required for most users. However, within multi-car households, approximately 50% of the second cars need to adapt less than one day per month. We also assess how users in two-car households adapt to a BEV replacing one of their ordinary cars. We find large heterogeneity in how users adapt, where some increase the use of the BEV compared to the replaced car, and some decrease it. From interview data we find that most households have experienced no actual problems with the range limitation, but most would prefer a range of 200 km. As a methodological contribution, we analyze 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. But when estimating the frequency of long-distance driving we find that Weibull and Gamma perform better than Log-Normal. Finally, we have extended the traditional driving data analysis beyond distance analysis to destination analysis. One of the results is that BEVs drive a significantly larger share of their driving to their most common destinations compared to a conventional car.