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

Abstract

Abstract

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 (COV_IMEP). Since the COV_IMEP is calculated from a set of IMEP data obtained by experiments, prediction of the COV_IMEP 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 COV_IMEP 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 COV_IMEP 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.