Predictive Cylinder Deactivation Control for Large Displacement Automotive Engines

Abstract

This paper presents a self-learning strategy to control the deactivation of cylinders in large displacement automotive engines. Cylinder deactivation is a useful strategy to reduce emissions and improve fuel consumption without limiting the maximum engine power. A cylinder deactivation algorithm has to balance the target of reducing emissions with drivability concerns. Changing the cylinder configuration at the wrong time or too often negatively affects drivability and fuel efficiency. The paper proposes a self-learning algorithm inspired by the Markov Chain formalism. The proposed algorithm predicts the future fuel consumption and determines the best cylinder configuration based on that prediction. The prediction algorithm is learnt online using past inputs and thus adapts to the specific driver and conditions. Two versions of the algorithm are proposed that use two different inputs. The approaches are validated in simulation on an engine test-bed, showing that the self-learning algorithm yields fuel savings up to 10% without affecting drivability in the New European Driving Cycle.