Towards Real-Time Combustion Phase Estimation for linear RCCI Model-Predictive Control Design

Amin Modabberian, Xiaoguo Storm, Aneesh Vasudev, Kai Zenger, Jari Hyvönen, Maciej Mikulski

Research output: Contribution to journalConference articleScientificpeer-review

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Reactivity controlled compression ignition (RCCI) technology has gained in popularity due to its ability to achieve low level NOx and soot emissions with relatively high brake thermal efficiency. However, control of RCCI combustion is a complex task. Nonetheless, this challenge can be overcome with model-based control design (MBCD). In this work, a linear physics-based time-varying RCCI combustion model was developed and improved with an addition of a start-of-combustion (SOC) model. The model we developed, which is capable of real-time simulations, can predict the combustion phasing, heat-release, and cylinder pressure of an RCCI marine engine. The model showcases the trend-wise, high accuracy estimation of cumulative heat-release and cylinder pressure. Additionally, it is able to predict combustion phasing parameters with an error of less than 1% for control design.
Original languageEnglish
Pages (from-to)3170-3177
Number of pages8
Issue number2
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventIFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023
Conference number: 22


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