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A stochastic design optimization methodology to reduce emission spread in combustion engines

Mourat, Kadir; Eckstein, Carola; Koch, Thomas


This paper introduces a method for efficiently solving stochastic optimization problems in the field of engine calibration. The main objective is to make more conscious decisions during the base engine calibration process by considering the system uncertainty due to component tolerances and thus enabling more robust design, low emissions, and avoiding expensive recalibration steps that generate costs and possibly postpone the start of production. The main idea behind the approach is to optimize the design parameters of the engine control unit (ECU) that are subject to uncertainty by considering the resulting output uncertainty. The premise is that a model of the system under study exists, which can be evaluated cheaply, and the system tolerance is known. Furthermore, it is essential that the stochastic optimization problem can be formulated such that the objective function and the constraint functions can be expressed using proper metrics such as the value at risk (VaR). The main idea is to derive analytically closed formulations for the VaR, which are cheap to evaluate and thus reduce the computational effort of evaluating the objective and constraints. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000137607
Veröffentlicht am 20.09.2021
DOI: 10.1007/s41104-020-00073-y
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Kolbenmaschinen (IFKM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 06.2021
Sprache Englisch
Identifikator ISSN: 2365-5127, 2365-5135
KITopen-ID: 1000137607
Erschienen in Automotive and engine technology
Verlag Springer
Band 6
Heft 1-2
Seiten 15–29
Vorab online veröffentlicht am 28.11.2020
Nachgewiesen in Dimensions
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