Beta-Distribution-Based Knock Probability Estimation, Control Scheme, and Experimental Validation for SI Engines

Abstract

The fuel efficiency and power output of spark ignition (SI) engines are closely related to the spark timing. Advancing the spark timing is usually used as an approach to increase the efficiency. However, under some operating conditions, advanced spark timing can trigger abnormal combustion, which causes knocking. To avoid cylinder damage and to increase the engine efficiency, feedback control, which addresses the knocking phenomenon as a stochastic process, is required. In this brief, a Bayesian estimate of knock probability is used to replace the maximum likelihood estimate in a likelihood-ratio-based knock control strategy. The beta distribution is used to represent the distribution of the knock probability estimate based on the independent and identically distributed property of knock events. The proposed control algorithm is validated on a full-scale test bench with a production SI engine and is compared with the conventional spark advance control approach and the maximum-likelihood-based approach. The results show that the proposed approach is able to control and maintain a knock probability close to the target and introduce a low dispersion of spark timing after convergence.