Non-Equiprobable Statistical Analysis of Misfires and Partial Burns for Cycle-to-Cycle Control of Combustion Variability

Abstract

Cycle-to-cycle combustion variability (CV) in spark ignition internal combustion engines is amplified at high levels of exhaust gas recirculation (EGR) by sporadic partial burn and misfire events. A non-equiprobable cycle classification method, based on the magnitude of the indicated mean effective pressure (IMEP), was developed to discern and study the deterministic and stochastic components of cyclic CV. The time series analysis of experimental combustion cycles suggested that the occurrence of high energy release cycles right after misfires is the only deterministic component between consecutive cycles. This predictable behavior results from the retained air and fuel from the incomplete combustion cycle to the next. On the other hand, this study shows that the occurrence of partial burn and misfire cycles is the product of the stochastic component of cyclic CV with statistical properties similar to a multinomial probability distribution. It is demonstrated that observation of partial burns can increase the probability of observing a misfire when the conditional probability is used as the metric. Based on these findings, future work will be able to use the observation of partial burns alone to control the upper bound on the probability of misfire events. To this end, different metrics are proposed to control directly and indirectly the probability of misfires, and their advantages and disadvantages for feedback combustion control are discussed.