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In this work, an experimental and analysis methodology was developed to evaluate the preignition propensity of fuels and engine operating conditions in an SI engine. A heated glow plug was introduced into the combustion chamber to induce early propagating flames. As the temperature of the glowplug varied, both the fraction of cycles experiencing these early flames and the phasing of this combustion in the engine cycle varied. A statistical methodology for assigning a single-value to this complex behavior was developed and found to have very good repeatability. The effects of engine operating conditions and fuels were evaluated using this methodology. While this study is not directly studying the so-called stochastic preignition or low-speed preignition problem, it studies one aspect of that problem in a very controlled manner.

Gasoline Direct Injection (GDI) has become the preferred technology for spark-ignition engines resulting in greater specific power output and lower fuel consumption, and consequently reduction in CO2 emission. However, GDI engines face a substantial challenge in meeting new and future emission limits, especially the stringent particle number (PN) emissions recently introduced in Europe and China. Studies have shown that the fuel used by a vehicle has a significant impact on engine out emissions. In this study, nine fuels with varying chemical composition and physical properties were tested on a modern turbo-charged side-mounted GDI engine with design changes to reduce particulate emissions. The fuels tested included four fuels meeting US certification requirements; two fuels meeting European certification requirements; and one fuel meeting China 6 certification requirements being proposed at the time of this work.

The goal of this study was to generate exhaust fast gas data that could be used to identify phenomena that occur before, during, and after stochastic preignition (SPI), also called low-speed preignition (LSPI), events. Crank angle resolved measurement of exhaust hydrocarbons, NO, CO, and CO2 was performed under engine conditions prone to these events. Fuels and engine operating strategies were varied in an attempt to understand similarities and differences in SPI-related behavior that may occur between them. Several different uncommon (typically occurring in less than 1% of engine cycles) features of the fast gas data were identified, and the correlations between them and SPI events were explored. Although the thresholds used to define and identify these observations were arbitrary, they provided a practical means of identifying behavior in the fast gas data and correlating it to SPI occurrence.

In this multi-phase study, fuel and lubricant effects on stochastic preignition (SPI) were examined. First, the behavior of fuels for which SPI data had previously been collected were characterized in terms of their combustion and emissions behavior, and correlations between these characteristics and their SPI behavior were examined. Second, new SPI data was collected for a matrix of fuels that was constructed to test and confirm hypotheses that resulted from interpretation of the earlier data in the study and from data in open literature. Specifically, the extent to which the presence of heavy components in the fuel affected SPI propensity, and the extent to which flame initiation propensity affected SPI propensity, were examined. Finally, the interaction of fuels with lubricants expected to exhibit a range of SPI propensities was examined.

In order to further understand the sequence of events leading to stochastic preignition in a spark-ignition engine, a methodology previously developed by the authors was used to evaluate the propensity of a wide range of fuels to establishing propagating flames under conditions representative of those at which stochastic preignition (SPI) occurs. The fuel matrix included single component hydrocarbons, binary mixtures, and real fuel blends. The propensity of each fuel to establish a flame was correlated to multiple fuel properties and shown to exhibit consistent blending behaviors. No single parameter strongly predicted a fuel’s propensity to establish a flame, while multiple reactivity-based parameters exhibited moderate correlation. A two-stage model of the flame establishment process was developed to interpret and explain these results.