Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3567
Publisher DOI: 10.1109/ACCESS.2020.3027499
Title: Predictability of vibration loads from experimental data by means of reduced vehicle models and machine learning
Language: English
Authors: Dostal, Leo 
Grossert, Helge Johannes 
Dücker, Daniel-André  
Grube, Malte 
Kreuter, Daniel 
Sandmann, Kai 
Zillmann, Benjamin 
Seifried, Robert  
Issue Date: 28-Sep-2020
Publisher: IEEE
Source: IEEE Access 8: 177180-177194 (2020)
Journal: IEEE access 
Abstract (english): 
Nowadays electric cars are in the spotlight of automotive research. In this context we consider data based approaches as tools to improve and facilitate the car design process. Hereby, we address the challenge of vibration load prediction for electric cars using neural network based machine learning (ML), a data-based frequency response function approach, and a hybrid combined model. We extensively study the challenging case of vibration load prediction of car components, such as the traction battery of an electric car. We show using experimental data from Fiat 500e and VWeGolf cars that the proposed ML approach is able to outperform the classical model estimation by means of ARX and ARMAX models. Moreover, we evaluate the performance of a hybrid-ML concept for combination of ML and ARMAX. Our promising results motivate further research in the field of vibration load prediction using machine learning based approaches in order to facilitate design processes.
URI: http://hdl.handle.net/11420/8452
DOI: 10.15480/882.3567
ISSN: 2169-3536
Institute: Mechanik und Meerestechnik M-13 
Document Type: Article
Project: Publikationsfonds 2020 
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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