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2021 | OriginalPaper | Chapter

Active Output Selection for an HEV Boost Maneuver

Authors : Adrian Prochaska, Julien Pillas, Bernard Bäker

Published in: 21. Internationales Stuttgarter Symposium

Publisher: Springer Fachmedien Wiesbaden

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Zusammenfassung

This paper presents the first real world application of an active output selection strategy, which selects the leading model based on a normalized model quality criterion. The strategy is compared to two other baselines. The algorithm identifies three models of static criteria, which are used for the drivability calibration of the boost maneuver of an 48V HEV. The driving maneuvers are conducted on a powertrain test bench. To validate the results, the experiments were conducted for multiple times. The results confirm analyses on generic toy examples, which indicated great advantages of this learning strategy. In this application example, the strategy saves an amount of 20–65% measurements, depending on which baseline is referenced.

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Metadata
Title
Active Output Selection for an HEV Boost Maneuver
Authors
Adrian Prochaska
Julien Pillas
Bernard Bäker
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-658-33521-2_16