Peyton Jones, James (Villanova University), Patel, Vatsal (Villanova University)

Functional Stochastic Modeling of Empirical Knock Sensor Signals

Scheduled for presentation during the Invited Session "MODELLING AND CONTROL METHODS FOR ADVANCED VEHICLE CONTROL" (MoBT5), Monday, August 29, 2022, 15:50−16:10, Pfhal Hall 202

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

Knock behaves a cyclically random process but also excites deterministic knock resonant behavior within any given cycle. While individual instances of the resonant response are readily acquired, the stochastic / cyclic variations of such signals (which also reflect the underlying knock process) are harder to quantify. In this work, a more complete model of this process is developed, capturing both the cyclic variability in the knock response as well as its functional resonant behavior. A recently developed alignment process is first used to evaluate the characteristic ensemble mean knock ‘signature’ of the data. A functional linearization about this ensemble mean is then used to decompose and model cyclic variations in terms of small variations in amplitude, frequency and phasing of the signal. The model is fitted to the data, encapsulating the stochastic variation of the knock signal within the stochastic variation of the estimated parameters. A residual analysis used to assess the goodness of fit as a function of crank angle, and the distribution and covariance of the estimated parameters is discussed.