On-board knock probability map learning–based spark advance control for combustion engines

This article presents an on-board map learning–based spark advance control framework for combustion engines. The proposed control framework addresses the knock probabilistic constrained thermal efficiency optimization problem with three layers. First, in the upper layer, maps of knock event distribution and thermal efficiency are learned with manifold pressure and combustion phase as inputs. Second, the middle layer generates the knock probability constrained optimal combustion phase reference that is subsequently tracked by a hypothesis test-based feedback controller. Third, the lower layer employs a partial likelihood-based knock controller that retards the spark advance in case of the frequent knock events. The key contributions of this work are the three-layer control framework and the knock event distribution map learning in the upper layer. The knock event is supposed to obey binomial distribution, and the distribution is modeled by beta distribution and learned in the perspective of Bayesian learning. Moreover, the normalization algorithm is proposed for online feedfoward map update. The proposed map learning–based spark advance control framework is experimentally validated in a test bench equipped with a spark-ignition engine.

Ion Current Based Spark Advance Management for Maximum Torque Production and Knock Control

The objective of the present work is the development of a closed-loop individual cylinder spark advance control strategy that allows maximizing torque production while keeping the knocking phenomenon at levels considered safe for the engine components. The research activity has consisted of several phases: the first one was focused on the analysis of the relationship between knocking level and indicated mean effective pressure. The main result of this preliminary phase is a methodology for identifying target values of the chosen in-cylinder pressure based knocking index. A subsequent phase of the work has been devoted to a correlation analysis between pressure-based knocking indexes and knocking indexes obtained by processing other combustion-related signals (engine block vibration and ion current), showing that the ion current based system that has been developed allows reaching high correlation levels. Finally, in order to achieve the target knocking levels, the spark advance control strategy proposed here consists of two parallel contributions: a slower, adaptive and statistically-based contribution, and a fast but range-limited term. The process of designing the controller has been particularly fast and cost-effective, due to the development of a specific software environment that allows verifying the performance the controller would achieve when applied to the actual engine. Such structure may be described as a software rapid control prototyping environment, since an experimental database has been used to reproduce in a simulation environment the response of the controlled system (the engine) coupled to the spark advance control system. The proposed control strategy has been successfully implemented on a V12 6.0 liter high performance engine, allowing to maximize output torque while protecting engine components from knock-related damage.