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In this paper a new knock control algorithm is developed based on a stochastic interpretation of the knock signal and on a control objective specified as a certain percentage of knocking cycles. Unlike previous ‘stochastic’ knock controllers, the new algorithm does not average or low pass filter the knock intensity signal and the transient response of the controller is consequently much faster. The performance of the new controller is compared in detail with the response of a traditional deterministic controller using a simple but effective knock simulation tool. The results show that the new controller is able to operate at a more advanced mean spark angle and that there is much less cyclic variance about this mean. The transient response to excess knocking events is as fast, or faster, than the conventional controller, though the rate of recovery from overly retarded conditions is slower.

Knock control strategies attempt to optimize the tradeoff between improving torque output and engine efficiency while also regulating knock intensity and protecting the engine from damage. This tradeoff must be made in a stochastic framework since knocking combustion behaves as a random process. This paper therefore examines the marginal and joint statistical properties of both knock intensity, and IMEP under knock limited conditions. Autocorrelation and Pearson chi-squared tests are also used to validate the cyclic independence of the data, or to identify prior cycle effects. The results and joint distribution give insight into the tri-variate relationship between knock intensity, IMEP, and spark advance, providing a foundation for improved knock/IMEP simulation and optimized controller design.