Search Results

Partially Premixed Combustion (PPC) has shown to be a promising advanced combustion mode for future engines in terms of efficiency and emission levels. The combustion timing should be suitably phased to realize high efficiency. However, a simple constant model based predictive controller is not sufficient for controlling the combustion during transient operation. This article proposed one learning based model predictive control (LBMPC) approach to achieve controllability and feasibility. A learning model was developed to capture combustion variation. Since PPC engines could have unacceptably high pressure-rise rates at different operation points, triple injection is applied as a solvent, with the use of two pilot fuel injections. The LBMPC controller utilizes the main injection timing to manage the combustion timing. The cylinder pressure is used as the combustion feedback. The method is validated in a multi-cylinder heavy-duty PPC engine for transient control.

Fumigation was studied in a 12 L six-cylinder heavy-duty engine. Port-injected ethanol was ignited with a small amount of diesel injected into the cylinder. The setup left much freedom for influencing the combustion process, and the aim of this study was to find operation modes that result in a combustion resembling that of a homogeneous charge compression ignition (HCCI) engine with high efficiency and low NOx emissions. Igniting the ethanol-air mixture using direct-injected diesel has attractive properties compared to traditional HCCI operation where the ethanol is ignited by pressure alone. No preheating of the mixture is required, and the amount of diesel injected can be used to control the heat release rate. The two fuel injection systems provide a larger flexibility in extending the HCCI operating range to low and high loads. It was shown that cylinder-to-cylinder variations present a challenge for this type of combustion.

As the number of actuators and sensors increases in modern combustion engines, the task of optimizing engine performance becomes increasingly complex. Efficient information processing techniques are therefore important, both for off-line calibration of engine maps, and on-line adjustments based on sensor data. In-cylinder pressure sensors are slowly spreading from laboratory use to production engines, thus making data with high temporal resolution of the combustion process available. The standard way of using the cylinder pressure data for control and diagnostics is to focus on a few important physical features extracted from the pressure trace, such as the combustion phasing CA50, the indicated mean effective pressure IMEP, and the ignition delay. These features give important information on the combustion process, but much information is lost as the information from the high-resolution pressure trace is condensed into a few key parameters.