Semi-physical state and parameter estimation of diesel combustion phases for real-time applications

Abstract

A model-based methodology is presented, which allows the estimation of the characteristic phases of diesel combustion using a semi-physical model approach combined with state and parameter estimation through extended Kalman filtering. The physical relation between the fuel injection and the characteristic diesel combustion phases, such as premixed, diffusive combustion and burn-out, are modeled separately by linear dynamic transfer functions formulated in crank angle frequency domain and transformed into state space representation. The resulting state variables are the released burning energy and its derivatives of each combustion phase. Associated crank angle constants determine the dynamics of the combustion phases and represent the rate parameters to be estimated. By incorporating further physical assumptions regarding the fuel path and air–fuel-mixing dynamics, the combustion phase parameters are estimated online for each working cycle. Cylinder pressure signals and online combustion analysis are used to determine the burn rate of the diesel engine at the test bench. Investigations have shown that the estimated rate parameters depend on the current engine operation point. They are estimated during measurements and stored in lookup tables through an online-learning method based on a fast recursive least squares estimation algorithm.