Therefore, the mobility behavior of the German population is analyzed, and possible localizations of charging stations are examined based on their functionality. After developing a simulation by means of assumptions for a best and a worst case scenario, a quantitative need of 40 million charging points and an overall investment of billion euros is calculated. Finally, the implemented simulation is evaluated and the results are reviewed regarding plausibility. Persistent Identifier of the first edition:. Social Media Mentions:.
Charging station just located at route;. For the scenario direct long distance, the EV usage potential had to be rated considering the following four options:. One charging station just located at route;. The questionnaire was divided into two versions, each with 6 of the 12 charging scenarios presented. This was necessary to not overwhelm the participants by a too long questionnaire. The selection of the questionnaire Version A or B was randomized for each participant. The sample was asked per use case which EV usage potential they subjectively perceive to be appropriate.
They were also asked to estimate the rate of occurrence per use case in their personally daily routine. For that, a weighting factor of each use case, depending on its occurrence rate, was calculated afterwards. The results per use case are presented in the next section.
These rates do not indicate variations in the perceived EV usage potential and therefore do not help identifying relevant scenarios for EV usage. The weighted EV usage potentials highly depend on the occurrence rate of the use case. In addition, the usage potentials are now highly diverting, depending on the travel purpose considered in the use case.
The weighted EV usage potential for all use cases regarding the scenario direct short distance varies from 1. Regardless of the charging location, the estimated partial EV usage potential is significantly lower for use cases with the travel purpose private errands priv.
Therefore, a charging station at a public shopping location seems to have a substantial benefit regarding the EV usage. Considering the scenario travel chain, the weighted partial EV usage potential, again, showed a high dependence of travel purpose and estimated EV usage potentials varying between 2. Use cases with the charging scenario direct long distance generally show lower perceived partial usage potentials descriptive wise. This is because of low estimated occurrence rates of this scenario, which lead to lower weighting of the estimated EV usage potential.
However, the weighted EV usage potential highly diverts between the two regarded travel purposes, going from 0. Figure 3. Furthermore, the number and location of charging options influence the usage potential. In summary, an increase in EV usage potential, caused by an optimal positioning of fast-charging infrastructure, is influenced by the travel purpose.
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Currently, optimal located charging stations for trips with shopping or leisure purpose seem to generate a perceived benefit regarding EV usage. Especially, the higher perceived EV usage potential regarding travel chains for shopping or leisure purpose, compared to business, results in the assumption that, for travel chains, charging locations close to shopping places and leisure spots seem to be likely favoring the decision for an EV. Furthermore, the usage potential seems to be influenced by the number and location of charging options.
Direct long distances with the necessity to recharge at route seem to be more comfortable when having several charging options that can be chosen from. Trips with leisure purpose also seem to be better manageable with an EV than trips for private errands, probably because of more perceived time flexibility that can be rearranged for a recharge.
As a conclusion, fast-charging stations should primarily be placed at destinations, and further on axes close to leisure facilities. However, participants in former studies mentioned that further location criteria such as necessary detour to the charging location or points of interest at charging locations also have an influence on the perceived usage potential of an EV from their perspective. In the next subsection, these further relevant location criteria are therefore discussed on an explorative basis. To examine the influence of relevant location criteria on the acceptance and usage of a fast-charging station, the approach of a conjoint modeling was used.
Seven male research assistants with focus on electric mobility took part in the workshop. With the help of three different fictive use cases, derived from common use cases regarding driving a vehicle, the experts were asked to name relevant charging location criteria that they would consider when thinking of that use case. Each use case was dealt with by two ro three experts. Finally, the as relevant defined criteria for each of the three use cases were discussed in the plenum.
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As a result, after post-hoc reduction, four relevant criteria, with three parameter values each, were defined for urban areas, and three relevant criteria, with three parameter values each, were defined for rural and highway areas. The selected criteria for the metropolitan region were, with the parameter values in brackets:. Necessary detour to the charging location 0.
Kind of point of interest POI at charging location shopping facility, sport facility, place to eat ;. As relevant selected criteria for the region axes were, with parameter values in brackets:.
Kind of point of interest POI at charging location shopping facility, place to eat, no point of interest ;. The participants were invited to rate the desirability of a charging location with a certain combination of location criteria. This rating gave a conclusion on the impact of each of these criteria. The method is called conjoint analysis [ 12 , 13 ].
With the help of this method, fictive charging locations with a randomly selected combination of several criteria parameters were created and presented to the participants. The participants have to rate the combination of the criteria—in this case, a fictive charging location. The method post-hoc concludes from an overall rating of the location to the rating of several criteria, which is possible because of the different combinations presented. For the metropolitan region, each fictive location consisted of one parameter regarding necessary detour, one regarding kind of POI, one regarding walking distance from charging station to POI and one regarding number of charging options, which makes in total 4 different criteria.
A combination example for the metropolitan region is presented in Table 2.
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For axes, each location consisted of three criteria with a certain parameter, each referring to one of the three parameters available for selection per criterion. The participants had to rate 20 different fictive charging locations for the metropolitan region and 10 different fictive charging locations for axes. Selected combinations were not put back to the pool. Once the pool was empty, all combinations were reactivated for a new selection process. This procedure ensured that all possible combinations were selected at least once during the study.
The results of the rating are presented in the next section. First analyses of possible group differences did not show significant effects except for one significant difference in the perceived importance of the criteria necessary detour to the charging location. This one low significant difference can be disregarded due to little importance for further analysis and to increase the sample size for better interpretation of the study results. For the metropolitan region, all four criteria were rated by the EV users and nonusers as rather important or important.
Therefore, all of them were included in the following examination.
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Considering the necessary detour to a charging location, the likelihood of a good rating increases by more than 11 times, if the detour is not longer than 0. Furthermore, the walking distance from charging station to POI should not be longer than 50 m. Compared to other presented distances, the likelihood of a good rating increases about two times for such a location walking distance. Furthermore, it does not matter, if the POI at the charging location is a shopping facility or a place to eat. However, a sports facility would significantly decrease the likelihood of a good rating.
Finally, the option of having three charging points at one location increases the likelihood of a good rating of this location by six times, compared to only one charging point available.