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Prognosefähigkeit von Vibrationlastdaten mittels reduzierter Fahrzeugmodelle und künstlicher Intelligenz

Elektrische Fahrzeuge stehen schon heute sowie zukünftig im Fokus der Automobilforschung. Für die Auslegung der Traktionsbatterie wird in diesem Zusammenhang die Bestimmung der Vibrationslasten benötigt. Wir untersuchen in der vorliegenden Arbeit wie datenbasierte Modelle zu diesem Zweck verwendet werden können. Dabei gehen wir im Besonderen auf LSTM neuronale Netze und ARMAX Prozesse ein. Außerdem wird deren hybride Kombination zur Vorhersage der Vibrationslasten untersucht. Die für die vorliegenden Untersuchungen benötigten Messdaten wurden auf einer Schlechtwegestrecke gesammelt, wobei Sensoren an verschiedenen Messpunkten der Batterie sowie an den Fahrzeugrädern angebracht waren. Als Testfahrzeuge wurden dabei ein VW eGolf und ein Fiat 500e verwendet.

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Reference: DVM-TAG-2023-136

Title: Prognosefähigkeit von Vibrationlastdaten mittels reduzierter Fahrzeugmodelle und künstlicher Intelligenz
Author(s): L. Dostal, M. Grube, D. A. Dücker, H. Grossert, D. C. Kreuter, K. Sandmann, B. Zillmann, R. Seifried
DOI: 10.48447/DVM-TAG-2023-136
Event: Mobilität der Zukunft – Bauteilzuverlässigkeit im digitalen Zeitalter - DVM-Tag 2023
Year of publication: 2023
Publication type: conference paper (PDF)
Language: German
Keywords: Automobiltechnik,maschinelles Lernen,Zeitreihenanalyse,Schwingungsbelastung,Elektrofahrzeuge

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Editor(s): Deutscher Verband für Materialforschung und -prüfung e.V.

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1

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€25.00

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