|Institution:||Mid Sweden University|
|Keywords:||alternative fuel vehicle; charging infrastructure deployment; gasoline stations; Green Highway; Electric Vehicle Service Equipment; Natural Sciences; Earth and Related Environmental Sciences; Environmental Sciences; Naturvetenskap; Geovetenskap och miljövetenskap; Miljövetenskap; International Master's Programme in Ecotechnology and Sustainable Development NEKAA 120 higher education credits; Internationellt masterprogram i ekoteknik och hållbar utveckling NEKAA 120 hp; Miljövetenskap MV1; Environmental Science MV1|
|Full text PDF:||http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-24098|
Abstract Electric Vehicles (EVs) are considered a more sustainable alternative vehicle because of their efficient electric motor when compared to internal combustion engines (ICE), and thus help to mitigate environmental problems and reduce fossil fuel dependency. In or-der to support drivers of plug-in hybrid electrical vehicles (PEVs), the installation and adequate distribution of Electric Vehicle Service Equipment (EVSE) is a major factor. The availability of EVSE is a vital requirement in order to charge the vehicle’s battery pack through connection to the electricity grid. This thesis evaluates the likely distribu-tion of a sufficient number of charging stations, measured as the demand of EVSE, for initial deployment in the E14 highway. This highway is also known as the Green High-way region, where a plan has been outlined with the aim to create a fleet of 15% EVs in the area by 2020.In order to model EVSE distribution, the first step was to complete a survey in 2012 on the population density and location of cities, along with the location of already estab-lished charging station locations on the Green Highway. The survey was done with ge-ography information survey (GIS) software. The second step was to create a map of the region. Based on the map, the initial estimate of EVSE locations on the Green Highway project plan was analyzed, as the third step. This was used as an initial analysis. The forth step was to use the location of current gasoline stations to provide as alternative pattern for the EVSE sites.It was observed that the network of gasoline stations correlates positively with population density. Through using these stations, the optimal location of the EVSEs was proposed. However, the model results do not provide for sufficient placement of EVSE sites where the population density is very low. In order to assess the different potential options, it was necessary to create analytical models in Arc-GIS, in which buffer zones were created with a variable size of 10, 15, 20 and 31 miles. This permitted allocation of a geographical area to estimate the optimum sites for charging stations. The resultsiiishowed that for a buffer zone of 10 miles, 28 charging stations were calculated, using buffer zone of 15 miles gives 18 stations, and a buffer zone of 20 miles results in 13 charging station sites. Notably, the estimate of the 20-mile buffer zone gives the same results as for the 50 km (31 miles) buffer zone for residential areas along E14. Therefore, the results show that the optimal design is to deploy 14 fast charging stations with three-phase DC, or 14 fast charging stations with three-phase AC, installed adjacent to the E14 road.