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Previous studies of gasoline direct-injection compression-ignition (GDCI) showed good potential for very high efficiency, low NOx, and low PM over the full speed-load range. Low-temperature combustion was achieved using multiple-late injection (MLI), intake boost, and cooled EGR. Advanced injection and valvetrain were key enablers. In the current study, a new piston was developed and matched with the injection system. Single-cylinder engine tests were conducted with the objective to reduce injection pressure, intake boost, and swirl levels. Results showed that ISFC could be further improved while maintaining low levels of NOx, PM, and combustion noise. Efficiency loss analysis indicated a very efficient thermodynamic process with greatly reduced heat losses. Injection parameters could be used to control combustion phasing with good combustion stability. Engine simulations were performed to develop a practical boost system for GDCI.

Continued improvement in the combustion process of internal combustion engines is necessary to reduce fuel consumption, CO2 emissions, and criteria emissions for automotive transportation around the world. In this paper, test results for the Gen3X Gasoline Direct Injection Compression Ignition (GDCI) engine are presented. The engine is a 2.2L, four-cylinder, double overhead cam engine with compression ratio ~17. It features a “wetless” combustion system with a high-pressure direct injection fuel system. At low load, exhaust rebreathing and increased intake air temperature were used to promote autoignition and elevate exhaust temperatures to maintain high catalyst conversion efficiency. For medium-to-high loads, a new GDCI-diffusion combustion strategy was combined with advanced single-stage turbocharging to produce excellent low-end torque and power. Time-to-torque (TT) simulations indicated 90% load response in less than 1.5 seconds without a supercharger.

Signal processing incorporating Artificial Neural Networks (ANN) has been shown to be well suited for modeling engine-related performance indicators [ 1 , 2 , 3 ] that require multi-dimensional parametric calibration space. However, to obtain acceptable accuracy, traditional ANN implementation may require processing resources beyond the capability of current engine controllers. This paper explores the practicality of implementing an ANN-based algorithm performing real-time calculations of the volumetric efficiency (VE) for an engine with variable valve actuation (phasing and lift variation). This alternative approach was considered attractive since the additional degree of freedom introduced by variable lift would be cumbersome to add to the traditional multi-dimensional table-based representation of VE.