摘要：AbstractMultimode combustion in automotive engines have the potential to increase fuel efficiency by choosing the best combustion mode at each operating condition. When switches among the combustion modes are neither instantaneous nor lossless, a mode switch can only be beneficial if the associated residence time in the target mode is long enough to compensate for the fuel penalty of the switch. An accurate engine load prediction over a short-term horizon is thus necessary for deciding on a beneficial mode switch. This paper addresses this. In the application of spark ignited (SI) and homogeneous charge compression ignition (HCCI) combustion modes, prior work quantified fuel efficiency benefits and mode switch penalties. In this paper, it is estimated that, to result in a fuel economy benefit, the residence time in HCCI mode is required to be longer than 1.2 s. If it is possible to accurately predict engine load over such a time horizon, an optimal mode switch decision could be made. To this end, five receding horizon prediction methods, based on artificial neural networks and polynomial extrapolation, are tested with vehicle measurement data and compared in terms of their accuracy in predicting engine load as well as HCCI entry and exit events. It is shown that although the prediction accuracy is very low for visitations of the HCCI regime around 1.2 s, all the methods are able to determine if an immediate entry of exit will occur, and if a HCCI visitation is going to be very short. This finding is applied in a basic mode switch decision structure.
