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A Method for the Quantification of Powertrain Electrification Impacts on Driving Dynamics

Kraft, Markus ; Rinderknecht, Stephan (2023)
A Method for the Quantification of Powertrain Electrification Impacts on Driving Dynamics.
In: World Electric Vehicle Journal, 2018, 9 (2)
doi: 10.26083/tuprints-00016424
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: A Method for the Quantification of Powertrain Electrification Impacts on Driving Dynamics
Language: English
Date: 21 November 2023
Place of Publication: Darmstadt
Year of primary publication: 2018
Place of primary publication: Basel
Publisher: MDPI
Journal or Publication Title: World Electric Vehicle Journal
Volume of the journal: 9
Issue Number: 2
Collation: 10 Seiten
DOI: 10.26083/tuprints-00016424
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

This paper discusses a novel simulation-based study quantifying the impacts of driving dynamics in the electrification of conventional powertrains into hybrid powertrains. Towards this aim, the Fourier amplitude sensitivity test (FAST) is used to facilitate sensitivity analysis. Design of experiments and artificial neural network methods are employed to approximate the solution space to ensure a computationally efficient application of the FAST. To demonstrate this method, a simulation-based study was conducted to evaluate the electrification impacts in a challenging driving dynamic investigation scenario.

Uncontrolled Keywords: case-study, parallel HEV, powertrain, simulation, vehicle performance
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-164243
Additional Information:

This article belongs to the Special Issue Selected Papers from The 30th International Electric Vehicles Symposium and Exhibition (Stuttgart, Germany)

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institute for Mechatronic Systems in Mechanical Engineering (IMS)
Date Deposited: 21 Nov 2023 13:38
Last Modified: 23 Nov 2023 13:28
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/16424
PPN: 513400508
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