Cost Analysis of Electric Vehicle Charging Stations and Estimation of Payback Periods With Artificial Neural Networks

dc.contributor.author Olcay K.
dc.contributor.author Cetinkaya N.
dc.date.accessioned 2024-02-16T14:42:21Z
dc.date.available 2024-02-16T14:42:21Z
dc.date.issued 2023
dc.description FAAC Bulgaria EAD en_US
dc.description 2023 IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2023 -- 23 November 2023 through 25 November 2023 -- 196150 en_US
dc.description.abstract In this study, the current number of electric vehicles charging stations (EVCS) and the projected increase in their numbers for two different scenarios, as outlined in the literature, have been analyzed, taking into consideration all kinds of charging station costs, to determine their payback periods. Cost calculations and revenue projections have been conducted based on the high growth scenario for charging stations to establish their respective payback periods. Artificial neural networks (ANN) were developed using these data, and payback periods were predicted according to the medium growth scenario. An equation was formulated using the current numbers of electric vehicles and the growth rates specified in the literature to determine the number of electric vehicles in the near future. Moreover, the energy consumption of electric vehicles currently utilized in the automotive industry was identified using the data obtained. All of these data were employed in the training of artificial neural networks. The source of income covering the charging station costs is derived from electricity sales made at the stations. The calculated payback periods based on the number of charging stations per vehicle provided in the study and the forecasts made using artificial neural networks indicate that the charging station payback periods will significantly decrease in the future, warranting careful consideration of the initial costs. © 2023 IEEE. en_US
dc.identifier.doi 10.1109/CIEES58940.2023.10378772
dc.identifier.isbn 9798350336917
dc.identifier.scopus 2-s2.0-85183585202
dc.identifier.uri https://doi.org/10.1109/CIEES58940.2023.10378772
dc.identifier.uri https://hdl.handle.net/20.500.13091/5142
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof CIEES 2023 - IEEE International Conference on Communications, Information, Electronic and Energy Systems en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject artificial neural networks en_US
dc.subject deep learning. en_US
dc.subject electric vehicle charging stations costs en_US
dc.subject energy consumption en_US
dc.subject Automotive industry en_US
dc.subject Charging (batteries) en_US
dc.subject Cost benefit analysis en_US
dc.subject Deep learning en_US
dc.subject Electric vehicles en_US
dc.subject Energy utilization en_US
dc.subject Investments en_US
dc.subject 'current en_US
dc.subject Charging station en_US
dc.subject Cost analysis en_US
dc.subject Cost calculation en_US
dc.subject Deep learning. en_US
dc.subject Electric vehicle charging en_US
dc.subject Electric vehicle charging station cost en_US
dc.subject Energy-consumption en_US
dc.subject High growth en_US
dc.subject Payback periods en_US
dc.subject Neural networks en_US
dc.title Cost Analysis of Electric Vehicle Charging Stations and Estimation of Payback Periods With Artificial Neural Networks en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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gdc.description.department KTÜN en_US
gdc.description.departmenttemp Olcay, K., Kutahya Dumlupinar University, Dumlupinar Vocational School, Kütahya, Turkey; Cetinkaya, N., Konya Technical University, Dept. of Electrical and Electronic Engineering, Konya, Turkey en_US
gdc.description.endpage 9
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality N/A
gdc.description.startpage 1
gdc.description.wosquality N/A
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gdc.opencitations.count 2
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gdc.scopus.citedcount 3
gdc.virtual.author Çetinkaya, Nurettin
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