Robust constrained optimization for RCCI engines using nested penalized particle swarm

Lu Xia, Bram de Jager (Corresponding author), M.C.F. (Tijs) Donkers, Frank P.T. Willems

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)
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Abstract

Reactivity controlled compression ignition (RCCI) is a promising combustion concept which uses two fuels to combine high thermal efficiencies and low engine-out NOx and soot emissions. The combustion concept relies on controlled auto-ignition and is sensitive for changing injection pressure, fuel quality, etc. Consequently, modeling and control of this complex combustion concept is not straightforward. In this work, Gaussian process regression is used to arrive at a data-driven model for a gasoline-diesel RCCI engine. This data-driven model is employed in a robust optimization approach that uses a nested particle swarm optimization. The designed (feedforward) control inputs maximize the efficiency of the RCCI engine while satisfying safety and emissions constraints under various disturbed conditions. In the simulation study, robust performance is obtained, and the robust efficiency is very similar to the efficiency under nominal condition.
Original languageEnglish
Article number104411
Number of pages11
JournalControl Engineering Practice
Volume99
DOIs
Publication statusPublished - Jun 2020

Funding

This work is supported by the research program “Towards a HiEff engine” through the Netherlands Organization for Scientific Research under STW project 14927. The authors would like to thank ir. Robbert Willems, from the Eindhoven University of Technology, The Netherlands, for his help with experiments. This work is supported by the research program ?Towards a HiEff engine? through the Netherlands Organization for Scientific Research under STW project 14927. The authors would like to thank ir. Robbert Willems, from the Eindhoven University of Technology, The Netherlands, for his help with experiments.

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Eindhoven University of Technology
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Stichting Voor De Technische Wetenschappen14927

    Keywords

    • Combustion engines
    • Fuel efficiency optimization
    • Gaussian process regression
    • Particle swarm optimization
    • Robust optimization

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