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A model for public fast charging infrastructure needs

: Gnann, Till; Goldbach, Daniel; Jakobsson, Niklas; Plötz, Patrick; Bennehag, Anders; Sprei, Frances

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World electric vehicle journal 8 (2016), No.4, pp.933-944
ISSN: 2032-6653
International Electric Vehicle Symposium (EVS) <29, 2016, Montréal>
Journal Article, Conference Paper, Electronic Publication
Fraunhofer ISI ()
charging infrastructure; electric vehicle; queuing model; stochastic occupancy rate of charging points

Plug-in electric vehicles can reduce GHG emissions although the low availability of public charging infrastructure combined with short driving ranges prevents potential users from adoption. The rollout and operation, especially of public fast charging infrastructure, is very costly. Therefore, policy makers, car manufacturers and charging infrastructure providers are interested in determining a number of charging stations that is sufficient. Since most studies focus on the placement and not on the determination of the number of charging stations, this paper proposes a model for the quantification of public fast charging points. We first analyze a large database of German driving profiles to obtain the viable share of plug-in electric vehicles in 2030 and determine the corresponding demand for fast charging events. Special focus lies on a general formalism of a queuing system for charging points. This approach allows us to quantify the capacity provided per charging point and the required quantity. Furthermore, we take a closer look on the stochastic occupancy rate of charging points for a certain service level and the distribution of the time users have to wait in the queue. When applying this model to Germany, we find about 15,000 fast charging points with 50 kW necessary in 2030 or ten fast charging point per 1,000 BEVs. When compared with existing charging data from Sweden, this is lower than the currently existing 36 fast charging points per 1,000 BEVs. Furthermore, we compare the models output of charging event distribution over the day with that of the real data and find a qualitatively similar load of the charging network, though with a small shift towards later in the day for the model.