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Generierung von Prfzyklen aus Flottendaten mittels bestrkenden Lernens

Generierung von Prfzyklen aus Flottendaten mittels bestrkenden Lernens

Brand
Springer Nature
Manufacturer
N/A
Part Number
0
GTIN
9783658442200
Condition
New
Product Description

Andr Ebel wertet Flottendaten eines batterieelektrischen Fahrzeuges hinsichtlich Fehlerbedingungen aus und generiert daraus unter Verwendung einer Gesamtfahrzeugsimulationsumgebung reprsentative Prfzyklen zur zeitlichen Rekonstruktion der Fehlerbedingungen. Anhand der Flottendatenauswertung mit Methoden des Maschinellen Lernens identifiziert der Autor das schdigende Nutzungsverhalten fehlerhafter Fahrzeuge. Zur Generierung von kundennahen Prfzyklen setzt er das tiefe Q-Lernen ein, ein Verfahren des bestrkenden Lernens. Die Kombination der Flottendatenauswertung mit der Prfzyklengenerierung trgt zur zielgerichteten und realittsnahen Erprobung von Antriebsstrngen bei.

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