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Special Issue on Future Powertrain Technologies: Editorial

Jardin, Philippe and Eßer, Arved and Rinderknecht, Stephan (2020):
Special Issue on Future Powertrain Technologies: Editorial.
In: Vehicles, 2 (4), pp. 574-575. MDPI, e-ISSN 26248921,
DOI: 10.3390/vehicles2040032,
[Article]

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.

Item Type: Article
Erschienen: 2020
Creators: Jardin, Philippe and Eßer, Arved and Rinderknecht, Stephan
Title: Special Issue on Future Powertrain Technologies: Editorial
Language: English
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.

Journal or Publication Title: Vehicles
Journal volume: 2
Number: 4
Publisher: MDPI
Uncontrolled Keywords: New powertrain concepts, optimization for powertrain design, naturalistic driving, machine learning, vehicle efficiency
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institute for Mechatronic Systems in Mechanical Engineering (IMS)
Date Deposited: 09 Oct 2020 06:25
DOI: 10.3390/vehicles2040032
Official URL: https://www.mdpi.com/2624-8921/2/4/32#cite
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