<p>Dilute combustion using exhaust gas recirculation (EGR) presents a cost-effective method for increasing the efficiency of spark-ignition engines. However, the maximum amount of EGR that can be used at a given condition is limited by a rapid increment of cycle-to-cycle variability (CCV). This phenomenon is caused by the reduction in the flame propagation speed, which can be counteracted by enriching the cycles where low-energy events are prone to occur. This study describes a methodology to design a model-based stochastic optimal controller to adjust the cycle-to-cycle fuel injection quantity in order to reduce CCV and further extend the dilute limit.&nbsp;Given the complexity and chaotic nature of combustion events, the controller was enhanced with&nbsp;a machine learning algorithm for&nbsp;online&nbsp;estimation of&nbsp;the statistical properties of combustion efficiency, which are needed to generate predictions for next-cycle events.&nbsp;The experimental results suggested that the dilute limit can be extended from 18.5% to 21% EGR fraction at an operating condition relevant for highway cruising. In addition, the proposed method was compared with a state-of-the-art artificial intelligence-based controller attempting the same CCV reduction goal. Results indicated that the proposed controller can achieve a larger CCV reduction with less fuel enrichment compared to previous methods.&nbsp;</p>

Next-Cycle Dilute Combustion Control Using Online Machine Learning

Paper Proceedings: https://asmedigitalcollection.asme.org/ICEF/ICEF2021/volume/85512

ASME 2021 Internal Combustion Engine Division Fall Technical Conference (ICEF2021)

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