Normal-gamma distribution–based stochastic knock probability control scheme for spark-ignition engines

Abstract

Spark timing, one of the essential parameters to control combustion in spark-ignition gasoline engines, is often advanced to optimize the power output and fuel economy. An overly advanced spark timing, or equivalently a large spark advance, however, can lead to severe knocking under heavy load engine operating conditions. In a trade-off between engine damage avoidance and power enhancement, the knock probability has to be regulated at a low percentage. Based on the observation that the logarithm of the knock intensity under steady operating conditions follows a normal distribution, in this research, a Bayesian knock probability estimation method is proposed using the normal-gamma distribution and the observed knock intensity. Based on the estimation, a spark advance control algorithm is also developed. The proposed knock probability control algorithm is validated on a full-scale test bench with a production spark-ignition engine. The results show that the proposed method is capable of regulating the knock probability to be close to the target percentage. With different parameter settings, the controller can further be configured to behave more aggressively or conservatively in knock probability estimation and regulation. In comparison with the conventional controller and the maximum likelihood–based controller, and in the tip-in/tip-out test, the proposed method also presents a quick response to transient engine operating conditions and a low spark advance dispersion after the spark advance converges close to the borderline.