A dual-threshold knock controller

Jones, James C. Peyton; Shayestehmanesh, Saeed; Frey, Jesse

doi:10.1177/1468087416676756

Cited by 8 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SA control logic of the conventional controller is similar to that in (17), where the only difference is that the retard gain Kret is fixed. The conventional method has been used for comparisons by Jones et al, 15 Zhao and Shen, 16 Peyton Jones et al, 19,26 Frey et al, 27 and Zhao et al 28 as a typical SA control method of production systems. Contrary to the deterministic methods that react to every knock event, the ML-based control method is a fully stochastic control method that adjusts SA according to the knock probability statistics of a relatively long time span.…”

Section: Experimental Validationsmentioning

confidence: 99%

Beta distribution-based knock probability map learning and spark timing control for SI engines

Zhao

Shen

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

In gasoline engines, the spark timing is often advanced to increase fuel economy under certain heavy load engine operating conditions. As a compromise between the risk of knock and the power output, spark timing is regulated at the boundary where a low knock probability is tolerated. Due to the stochasticity of binary knock events, it is necessary to have a large number of engine cycles for probability estimations, which can slow down the response speed of a controller to operating condition changes. To speed up the spark timing regulation and to reduce the spark timing variance, in this article, a knock probability feedforward map learning method and a spark timing control method are proposed under a unified framework. A learning method that applies the beta distribution is the key contribution of this work. The beta distribution in the map learning part is used to describe knock probabilities with uncertainties and to determine the next engine operating condition for sampling and map learning. In the spark timing method, the beta distribution is applied in the conventional control method to adjust the control gains. The proposed methods are experimentally validated on a test bench equipped with a production Toyota 1.8 L, 4-cylinder SI engine.

show abstract

Section: Experimental Validationsmentioning

confidence: 99%

Beta distribution-based knock probability map learning and spark timing control for SI engines

Zhao

Shen

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…The dual-threshold knock controller, presented in Peyton Jones et al, 11 addresses these issues by using different thresholds to identify ‘knocking’ and ‘non-knocking’ events. If the knock intensity falls above the ‘knocking’ threshold, the spark is retarded as before, but the spark is only advanced if the knock intensity falls below a new ‘non-knocking’ threshold.…”

Section: Multi-threshold Knock Control Lawmentioning

confidence: 99%

“…9,10 Recently, a new ‘dual-threshold’ knock controller was presented where the binary knocking/not-knocking classification used by most knock-event-based controllers was extended to a ternary knocking/not-knocking/indeterminate classification using two knock thresholds. 11 This reduced the chance of misclassifying knocking cycles as non-knocking or vice versa, enabling the controller gains (and hence speed of response) to be increased slightly without other adverse effects. More generally, it is also possible to envisage a multi-threshold knock controller defining different control actions for different ranges of knock intensity input amplitude.…”

Section: Introductionmentioning

confidence: 99%

Computing the closed-loop characteristics of a generalized multi-threshold knock controller

Shayestehmanesh

Jones

Frey

2017

International Journal of Engine Research

Self Cite

View full text Add to dashboard Cite

Most knock controllers respond to knock events which are defined according to some threshold knock intensity. Multi-threshold knock events offer more informative feedback since they encode not just the occurrence of knock events but also some measure of their intensity. While this has the potential for improved control, it is hard to assess the extent to which any benefits are truly realized because (in common with all knock control systems) the results of any single experiment or simulation depends on the random arrival of knock events in that instance. In this article, methods are developed instead to compute the statistical properties of the closed-loop response of a general multi-threshold knock controller, thereby providing a much more complete and rigorous characterization of its performance than has previously been possible. The method is applied to single- and dual-threshold knock controllers and used to provide a rigorous comparison of the transient and steady-state performance of these different control laws. The method can also be used as a calibration aid to assess the effects of different controller gains in reliable, repeatable fashion.

show abstract

“…Further work, use the likelihood ratio of observed events to scale the advance and retard gains [16]. Recently, a new dual-threshold knock controller was presented in [17] where the binary knocking and no-knocking classification used by most knock-eventbased controllers was extended to a ternary knocking/notknocking/indeterminate classification using two knock thresholds, this reduced the chance of misclassifying knocking cycles as non-knocking. Reducing the misclassification of knocking events allows to increase the controller gain, which leads to faster response [18].…”

Section: Introductionmentioning

confidence: 99%