DC FieldValueLanguage
dc.contributor.advisorAjanovic, Amela-
dc.contributor.authorWijdeveld, Robertus Everard Johannes Bernardus-
dc.date.accessioned2020-06-30T02:19:35Z-
dc.date.issued2012-
dc.identifier.urihttps://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-51666-
dc.identifier.urihttp://hdl.handle.net/20.500.12708/9870-
dc.description.abstractElectric vehicles are receiving renewed and growing interest from the general public. Therefore more comprehensive knowledge of the driver's behaviour and the impact of electric vehicles on the power generation, electrical grid load and distribution installations are necessary. Data concerning vehicle charge profiles and battery load profiles are required for the estimation of the daily grid load in order to evaluate the impact on the electricity infrastructure. The scope and main objective of this study is to determine the assumptions and methodology to convert the mobility behaviour of a specific mobility group into their prospective charge behaviour and the battery load profiles. The charge profile gives an indication of the actual power demand from an electric vehicle at a certain time of the day. The battery load profile gives the average daily power demand based on the mobility behaviour translated to one vehicle. Driving and charging behaviour vary significantly across different mobility groups, along with vehicle energy consumption. With these results one can predict the impact from electric vehicles on the electrical grid. This study made use of the mobility survey of the Netherlands which is carried out by the Dutch Centre for Transport and Navigation and contains general mobility data from the total Dutch population divided in different behavioural groups, which are: commuters, business, shopping, private errands, and leisure. This data includes the trip distance, number of daily trips, time of arrival at certain destinations and parking times. For each mobility group, this study has developed different charge scenarios which are based on the most common drive cycles. From these drive cycles, the energy use of the average trip distance and the battery state of charge are calculated on an hourly base. Consequently, a model has been developed in which the distribution of the mobility data from a defined mobility group is combined with the battery power demand of their drive cycle. Out of this model, the actual vehicle charge load profile and the average daily battery load profile from an electric vehicle can be determined. The majority of all vehicle trips are relatively short and are between 5 and 10 km (22%). On average, 80% of all trips are below 20 km and 95% of all trips are shorter than 75 km. This enables an electric vehicle a return trip home without a recharge. Therefore charging after each trip, "anywhere anytime ", is generally not required. Only during occasional long trip distances (5% of all trips), charging at location would be necessary. On average, only 5 trips a year are longer than 150 km which is more or less the range of an electric vehicle and requires charging during the trip. A vehicle is on average only used 50 minutes a day; this means it is parked 96.5% of the time, enabling enough time to recharge the battery. People may drive longer distances to locations where they intend to park their car for a longer time. Table ES-1 below gives the main outcome from the Dutch mobility survey for different mobility groups. Applying these models, it can be concluded that the vehicle charge profiles indicate that charging is only useful in combination with long parking times and is not only limited by the end of the day at home. People want to minimize range anxiety by charging when there is a possibility. Therefore the establishment of a charging infrastructure should not only be focused on charging at home, but also at locations with long parking times. Another conclusion is that low power public charge stations (3.7 kW) at shopping or private errand locations may not be very appropriate because of the short drive cycles, short parking times and the time necessary to charge any significant amount of energy. The vehicle charge profile indicates in these scenarios a low power demand when the electric vehicle still contains a high battery state of charge. The highest average daily power demand is 1.7 kW (3.7 kW charge) and 3 kW (11 kW charge) per vehicle for commuters charging at work with a battery state of charge <40%. This is because of the very concentrated arrival time of the majority of the people at work. Other mobility group's shows more scattered arrival times and starting the recharge will not happen at one specific time, but will be more dispersed over the day. This gives a lower average daily power demand.de
dc.formatX, 80 Bl.-
dc.languageEnglish-
dc.language.isoen-
dc.titleA study to estimate electric vehicle load profiles based on the Dutch mobility behaviouren
dc.typeThesisen
dc.typeHochschulschriftde
tuw.publication.orgunitE017 - Weiterbildungszentrum der TU Wien-
dc.type.qualificationlevelDiploma-
dc.identifier.libraryidAC09049382-
dc.description.numberOfPages80-
dc.identifier.urnurn:nbn:at:at-ubtuw:1-51666-
dc.thesistypeMasterarbeitde
dc.thesistypeMaster Thesisen
item.languageiso639-1en-
item.openairetypeThesis-
item.openairetypeHochschulschrift-
item.fulltextwith Fulltext-
item.cerifentitytypePublications-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
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