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Special issue on future powertrain technologies: editorial

Jardin, Philippe ; Eßer, Arved ; Rinderknecht, Stephan (2020)
Special issue on future powertrain technologies: editorial.
In: Vehicles, 2 (4)
doi: 10.3390/vehicles2040032
Artikel, Bibliographie

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Kurzbeschreibung (Abstract)

Synthetic Driving Cycles have been used in numerous studies to describe a certain driving profile of relevance. An important purpose of synthetic cycles is to limit the necessary time on a test-rig or to reduce the computational effort within simulations, which is achieved by compressing a larger amount of gathered operating data from a certain vehicle or a vehicle fleet to a necessary minimum. Interestingly, despite the intensive use of the synthetic driving cycles, there is only limited literature on the validation of using synthetic driving cycles. Therefore, the scope of this work is to further investigate under which conditions synthetic driving cycles can be used to replace the entirety of the relevant operating data in the evaluation of a vehicle’s consumption. We apply a longitudinal vehicle simulation model to calculate the fuel and electric consumption of vehicles with different powertrain concepts on many generated synthetic driving cycles for different compression rates. We then compare that to the consumption if considering the original driving data. A legislative driving cycle (WLTC) as well as naturalistic driving data sets are used for the evaluation. The results show, that synthetic driving cycles allow for a compact representation of the original data sets but possible compression rates depend on the specific driving data. The presented two-step process can be extended to a generalized validation process for the use of synthetic driving cycles.

Typ des Eintrags: Artikel
Erschienen: 2020
Autor(en): Jardin, Philippe ; Eßer, Arved ; Rinderknecht, Stephan
Art des Eintrags: Bibliographie
Titel: Special issue on future powertrain technologies: editorial
Sprache: Englisch
Publikationsjahr: 30 September 2020
Verlag: MDPI
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Vehicles
Jahrgang/Volume einer Zeitschrift: 2
(Heft-)Nummer: 4
DOI: 10.3390/vehicles2040032
URL / URN: https://www.mdpi.com/2624-8921/2/4/32#cite
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Kurzbeschreibung (Abstract):

Synthetic Driving Cycles have been used in numerous studies to describe a certain driving profile of relevance. An important purpose of synthetic cycles is to limit the necessary time on a test-rig or to reduce the computational effort within simulations, which is achieved by compressing a larger amount of gathered operating data from a certain vehicle or a vehicle fleet to a necessary minimum. Interestingly, despite the intensive use of the synthetic driving cycles, there is only limited literature on the validation of using synthetic driving cycles. Therefore, the scope of this work is to further investigate under which conditions synthetic driving cycles can be used to replace the entirety of the relevant operating data in the evaluation of a vehicle’s consumption. We apply a longitudinal vehicle simulation model to calculate the fuel and electric consumption of vehicles with different powertrain concepts on many generated synthetic driving cycles for different compression rates. We then compare that to the consumption if considering the original driving data. A legislative driving cycle (WLTC) as well as naturalistic driving data sets are used for the evaluation. The results show, that synthetic driving cycles allow for a compact representation of the original data sets but possible compression rates depend on the specific driving data. The presented two-step process can be extended to a generalized validation process for the use of synthetic driving cycles.

Freie Schlagworte: new powertrain concepts, optimization for powertrain design, naturalistic driving, machine learning, vehicle efficiency
Fachbereich(e)/-gebiet(e): 16 Fachbereich Maschinenbau
16 Fachbereich Maschinenbau > Institut für Mechatronische Systeme im Maschinenbau (IMS)
Hinterlegungsdatum: 09 Okt 2020 06:25
Letzte Änderung: 15 Jan 2024 09:33
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