Fuel-Efficient Model-Based Optimal MIMO Control for PCCI Engines

Abstract Recent research in modern combustion technologies, like partial homogeneous charge compression ignition (PCCI), demonstrates the capability of reducing pollutant emissions, e.g. soot and NOX. In addition to this advantage, a possibility to reduce fuel consumption and noise production by model-based optimal control is presented in this paper. In order to understand the basic properties of the PCCI mode, process measurements were conducted using a slightly modified series diesel engine. Control variables are engine combustion parameters: the indicated mean effective pressure, the combustion average and the maximum gradient of the cylinder-pressure. Control inputs are the parameters: quantity of injected fuel, start of injection and the intake manifold fraction of recirculated exhaust gas. The process has very fast, almost proportional behaviour over the engine's working cycles. Focusing on the static behaviour of the process, a nonlinear neural network model is used for identification. Successive linearization of the nonlinear network is used to build an affine internal controller model for the actual operating point. The presented controller structure is able to consider constraints by individual formulation of the cost function. With this configuration the closed-loop process is able to track the combustion setpoints with high control quality with minimal possible fuel consumption and combustion noise.