Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 918207

A Data-Driven Statistical Approach for Extending Electric Vehicle Charging Infrastructure


Pevec, Dario; Babić, Jurica; Kayser, Martin A.; Carvalho, Arthur; Ghiassi-Farrokhfal, Yashar; Podobnik, Vedran
A Data-Driven Statistical Approach for Extending Electric Vehicle Charging Infrastructure // International journal of energy research, 42 (2018), 9; 3102-3120 doi:10.1002/er.3978 (međunarodna recenzija, članak, znanstveni)


Naslov
A Data-Driven Statistical Approach for Extending Electric Vehicle Charging Infrastructure

Autori
Pevec, Dario ; Babić, Jurica ; Kayser, Martin A. ; Carvalho, Arthur ; Ghiassi-Farrokhfal, Yashar ; Podobnik, Vedran

Izvornik
International journal of energy research (0363-907X) 42 (2018), 9; 3102-3120

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
charging infrastructure ; data science ; electric vehicles ; green transportation ; energy informatics

Sažetak
Current trends suggest that there is a substantial increase in the overall usage of electric vehicles (EVs). This, in turn, is causing drastic changes in the transportation industry and, more broadly, in business, policy making, and society. One concrete challenge brought by the increase in the number of EVs is a higher demand for charging stations. This paper presents a methodology to address the challenge of EV charging station deployment. The proposed methodology combines multiple sources of heterogeneous real-world data for the sake of deriving insights that can be of a great value to decision makers in the field, such as EV charging infrastructure providers and/or local governments. Our starting point is the business data, i.e., data describing charging infrastructure, historical data about charging transactions, and information about competitors in the market. Another type of data used are geographical data, such as places of interest located around chargers (e.g., hospitals, restaurants, and shops) and driving distances between available chargers. The merged data from different sources are used to predict charging station utilization when EV charging infrastructure and/or contextual data change, e.g., when another charging station or a place of interest is created. Based on such predictions, we suggest where to deploy new charging stations. We foresee that the proposed methodology can be used by EV charging infrastructure providers and/or local governments as a decision support tool that prescribes an optimal area to place a new charging station while keeping a desired level of utilization of the charging stations. We showcase the proposed methodology with an illustrative example involving the Dutch EV charging infrastructure through the period from 2013 to 2016. Specifically, we prescribe the optimal location for new ELaadNL charging stations based on different objectives such as maximizing the overall charging network utilization and/or increasing the number of chargers in scarcely populated areas.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Tehnologija prometa i transport, Ekonomija



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Vedran Podobnik (autor)

Avatar Url Jurica Babić (autor)

Avatar Url Dario Pevec (autor)

Citiraj ovu publikaciju

Pevec, Dario; Babić, Jurica; Kayser, Martin A.; Carvalho, Arthur; Ghiassi-Farrokhfal, Yashar; Podobnik, Vedran
A Data-Driven Statistical Approach for Extending Electric Vehicle Charging Infrastructure // International journal of energy research, 42 (2018), 9; 3102-3120 doi:10.1002/er.3978 (međunarodna recenzija, članak, znanstveni)
Pevec, D., Babić, J., Kayser, M., Carvalho, A., Ghiassi-Farrokhfal, Y. & Podobnik, V. (2018) A Data-Driven Statistical Approach for Extending Electric Vehicle Charging Infrastructure. International journal of energy research, 42 (9), 3102-3120 doi:10.1002/er.3978.
@article{article, year = {2018}, pages = {3102-3120}, DOI = {10.1002/er.3978}, keywords = {charging infrastructure, data science, electric vehicles, green transportation, energy informatics}, journal = {International journal of energy research}, doi = {10.1002/er.3978}, volume = {42}, number = {9}, issn = {0363-907X}, title = {A Data-Driven Statistical Approach for Extending Electric Vehicle Charging Infrastructure}, keyword = {charging infrastructure, data science, electric vehicles, green transportation, energy informatics} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Uključenost u ostale bibliografske baze podataka:


  • CA Search (Chemical Abstracts)
  • Compendex (EI Village)
  • Fluidex (Fluid Engineering Abstracts)
  • GeoRef
  • Geoarchive
  • INSPEC
  • ChemWeb
  • EnCompass Lit
  • Geotitles
  • Groundwater & Soil Contamination Database
  • IAOR
  • ICONDA
  • JCReports/Science Edition
  • PASCAL Database
  • VINITI


Citati