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Dilute combustion with exhaust gas recirculation (EGR) in spark-ignition engines presents a cost-effective method for achieving higher levels of engine efficiency. At high levels of EGR, however, cycle-to-cycle variability (CCV) of the combustion process is exacerbated by sporadic occurrences of misfires and partial burns. Previous studies have shown that temporal deterministic patterns emerge at such conditions and certain combustion cycles have a significant influence over future events. Due to the complexity of the combustion process and the nature of CCV, harnessing all the deterministic information for control purposes has remained challenging even with physics based 0-D, 1-D, and high-fidelity computational fluid dynamics (CFD) models. In this study, we present a data-driven approach to optimize the combustion process by controlling CCV adjusting the cycle-to-cycle fuel injection quantity.

Spark-ignition (SI) engines can derive substantial efficiency gains from operation at high dilution levels, but sufficiently high-dilution operation increases the occurrence of misfires and partial burns, which induce higher levels of cyclic-variability in engine operation. This variability has been shown to have both stochastic and deterministic components, with residual fraction impacts on charge composition being the major source of the deterministic component through its non-linear effect on ignition and flame propagation characteristics. This deterministic coupling between cycles offers potential for next-cycle control approaches to allow operation near the edge of stability. This paper aims to understand the effect of spark strategies, specifically the use of a second spark (restrike) after the main spark, on the deterministic coupling between engine cycles by operating at high dilution levels using both excess air (i.e. lean combustion) and EGR.