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Tytuł artykułu

Comparison of the cumulative energy demand of BEV’s and FCEV’s in their long-term operation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents a method of using the theory of cumulative energy demand to assess this demand in long-term operation of vehicles with a mileage forecast of up to 350,000 km. Based on the results of operational "consumption" of energy and taking into account the energy "costs" of obtaining it, a comparison of the currently popular BEV’s (Battery Electric Vehicles) and FCEV’s (Fuel Cell Electric Vehicles) was presented. The question arises how much energy must be used to propel vehicles in their natural operation. After calculating using available data, the answer is - by operating FCEV’s on average, two times more electricity is needed than by operating BEV’s.
Czasopismo
Rocznik
Strony
24--29
Opis fizyczny
Bibliogr. 14 poz., 1 il. kolor., wykr.
Twórcy
  • Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Poland
Bibliografia
  • [1] Aksoy A, Küçükoğlu İ, Ene S, Öztürk N. Integrated emission and fuel consumption calculation model for green supply chain management. Procedia - Social and Behavioral Sciences. 2014;109:1106-1109. https://doi.org/10.1016/j.sbspro.2013.12.595
  • [2] Balcı Ö, Karagöz Y, Gezer O, Kale S, Köten H, Pusat S et al. Numerical and experimental investigation of fuel consumption and CO2 emission performance for a parallel hybrid vehicle. Alexandria Eng J. 2021;60(4):3649-3667. https://doi.org/10.1016/j.aej.2021.02.025
  • [3] Braun A, Rid W. Energy consumption of an electric and an internal combustion passenger car. A comparative case study from real world data on the Erfurt circuit in Germany. Transp Res Procedia. 2017;27:468-475. https://doi.org/10.1016/j.trpro.2017.12.044
  • [4] Girtler J. Possibility of estimating the reliability of diesel engines by applying the theory of semi-Markov processes and making operational decisions by considering reliability of diagnosis on technical state of this sort of combustion engines. Combustion Engines. 2015;163(4):57-66. https://doi.org/10.19206/CE-116857
  • [5] Greene DL, Liu J, Khattak AJ, Wali B, Hopson JL, Goeltz R. How does on-road fuel economy vary with vehicle cumulative mileage and daily use? Transport Res D-Tr E. 2017;55:142-161. https://doi.org/10.1016/j.trd.2017.06.004
  • [6] Katreddi S, Thiruvengadam A. Trip based modeling of fuel consumption in modern heavy-duty vehicles using artificial intelligence. Energies. 2021;14(24):8592. https://doi.org/10.3390/en14248592
  • [7] Kropiwnicki J, Kortas P. Potentials for fuel consumption reduction by using electronic driver assistance systems. Combustion Engines. 2013;154(3):250-256.
  • [8] Li W, Stanula P, Egede P, Kara S, Herrmann C. Determining the main factors influencing the energy consumption of electric vehicles in the usage phase. Procedia CIRP. 2016; 48:352-357. https://doi.org/10.1016/j.procir.2016.03.014
  • [9] Sitnik LJ. Energy demand assessment for long term operation of vehicles. SAE Technical Paper 2020-01-2165. 2020. https://doi.org/10.4271/2020-01-2165
  • [10] Sitnik L. Engine eco fuels. Wroclaw Publishing House (in Polish). Wroclaw 2004.
  • [11] Spritmonitor. https://www.spritmonitor.de/
  • [12] Travesset-Baro O, Rosas-Casals M, Jover E. Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra. Transport Res D-Tr E. 2015;34:16-26. https://doi.org/10.1016/j.trd.2014.09.006
  • [13] Usmanov U, Ruzimov S, Tonoli A, Mukhitdinov A. Modeling, simulation and control strategy optimization of fuel cell hybrid electric vehicle. Vehicles. 2023;5(2):464-481. https://doi.org/10.3390/vehicles5020026
  • [14] Xu Y, Zhang H, Yang Y, Zhang J, Yang F, Yan D et al. Optimization of energy management strategy for extended range electric vehicles using multi-island genetic algorithm. J Energy Storage. 2023;61:106802. https://doi.org/10.1016/j.est.2023.106802
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b5abb5fb-e664-4405-86f4-51a13d27dd7d
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