Abstract:
An engine control system includes a prediction module that, during an exhaust stroke of a first cylinder of an engine, determines a predicted intake manifold pressure at an end of a next intake stroke of a second cylinder following the first cylinder in a firing order of the cylinders. An air per cylinder (APC) module determines a predicted mass of air that will be trapped within the second cylinder at the end of the next intake stroke of the second cylinder based on the predicted intake manifold pressure. A fueling module controls fueling of the second cylinder during the next intake stroke based on the predicted mass of air.
Abstract:
A torque requesting module generates a torque request for an engine based on driver input. A model predictive control (MPC) module: identifies sets of possible target values based on the torque request, each of the sets of possible target values including target effective throttle area percentage; determines predicted operating parameters for the sets of possible target values, respectively; determines cost values for the sets of possible target values, respectively; selects one of the sets of possible target values based on the cost values; and sets target values based on the possible target values of the selected one of the sets, respectively, the target values including a target pressure ratio across the throttle valve. A target area module determines a target opening area of the throttle valve based on the target effective throttle area percentage ratio. A throttle actuator module controls the throttle valve based on the target opening.
Abstract:
Computational models and calculations relating to trapped and scavenged air per cylinder (APC) improve scavenging and non-scavenging operational modes of internal combustion engines as well as the transition there-between. Data from sensors which include engine speed, manifold air pressure, barometric pressure, crankshaft position, and valve state are provided to a pair of artificial neural networks. A first neural network utilizes this data to calculate the nominal volume of gas, i.e., air trapped in the cylinder. A second neural network utilizes this data to calculate the trapping ratio. The output of the first network is utilized with the ideal gas law to calculate the actual mass of trapped APC. The actual mass of trapped APC is also divided by the trapping ratio calculated by the second network to determine the total APC and is further utilized to calculate the scavenged APC by subtracting the trapped APC from the total APC.
Abstract:
A system according to the principles of the present disclosure includes a model predictive control (MPC) module and an actuator module. The MPC module generates predicted parameters based on a model of a subsystem and a set of possible target values. The MPC module generates a cost for the set of possible target values based on the predicted parameters and at least one of weighting values and references values. The MPC module adjusts the at least one of the weighting values and the reference values based on a desired rate of change in an operating condition of the subsystem. The MPC module selects the set of possible target values from multiple sets of possible target values based on the cost. The actuator module adjusts an actuator of the subsystem based on at least one of the target values.