Robust constrained optimization for RCCI engines using nested penalized particle swarm

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

doi:10.1016/j.conengprac.2020.104411

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

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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 language	English
Article number	104411
Number of pages	11
Journal	Control Engineering Practice
Volume	99
DOIs	https://doi.org/10.1016/j.conengprac.2020.104411
Publication status	Published - 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.

Funders	Funder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Eindhoven University of Technology
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Stichting voor de Technische Wetenschappen	14927

Keywords

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

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10.1016/j.conengprac.2020.104411

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title = "Robust constrained optimization for RCCI engines using nested penalized particle swarm",

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.",

keywords = "Combustion engines, Fuel efficiency optimization, Gaussian process regression, Particle swarm optimization, Robust optimization",

author = "Lu Xia and {de Jager}, Bram and Donkers, {M.C.F. (Tijs)} and Willems, {Frank P.T.}",

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AU - Xia, Lu

AU - de Jager, Bram

AU - Donkers, M.C.F. (Tijs)

AU - Willems, Frank P.T.

PY - 2020/6

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N2 - 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.

AB - 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.

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Robust constrained optimization for RCCI engines using nested penalized particle swarm

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Keywords

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