Real-time estimation of combustion variability for model-based control and optimal calibration of spark ignition engines

A method for indirect real-time estimation of combustion variability by substitutive measurements is investigated in this paper. An impetus for improving combustion stability, particularly at near-idle conditions, is provided by increasing driver sensitivity to engine noise, vibration, and harshness. The combustion stability is generally quantified by the cycle-by-cycle variations, and represented by the coefficient of variation in the indicated mean effective pressure (COVIMEP). Since the COVIMEP is calculated from a set of IMEP data obtained by experiments, prediction of the COVIMEP in real time from measurements of IMEP is impossible. Instead, an indirect prediction methodology of combustion variability is proposed on the basis of statistical analysis of substitutive measurements. A set of relevant exploratory variables is determined first. Then, trends of the COVIMEP are investigated to determine the regression model, and an exponential form proved to be the best. A systematic regression analysis procedure based on statistical analysis enables the simplest equations to be found while maintaining accuracy. The results demonstrate that the COVIMEP behaviour can be captured as a function of 10-90 per cent burn duration and manifold absolute pressure. The implementation can utilize real-time pressure measurements if the engine is equipped with pressure transducers, or virtual sensing of 10-90 per cent burned using a neural network trained a priori on measured or predicted data. The resulting regression equations derived with and without the charge motion valve enable the estimation of the combustion variability for optimal calibration or model-based control.