An engine model construction method includes generating test patterns in which a plurality of manipulated variables used for an engine test are changed with time, correcting the test patterns based on first coverage of a first space of manipulated variables are allowed to take and second coverage of a second space of change rate values of the manipulated variables are allowed to take, acquiring pieces of time series data of operation amounts of the manipulated variables and controlled amounts with respect to the manipulated variables by performing an engine test using the corrected test patterns, and constructing a first engine model by performing machine learning on training data in which the corrected test patterns are adopted as input and the pieces of time series data are adopted as correct answers, by a processor.

A CPU substitutes a difference between a crank-side speed that is a rotation speed of a crankshaft and a downstream-side speed that is a speed of a portion, opposite from the crankshaft, in a damper into a differential speed. The CPU calculates a torsion angle through a process of integrating the differential speed. The CPU calculates a torsion speed component that is a speed component of the crankshaft due to torsion of the damper based on a process of integrating a value obtained by multiplying the torsion angle by an elastic modulus, and calculates a time that is a variable indicating a speed of the crankshaft, used to determine a misfire, based on the torsion speed component. The CPU subtracts a value obtained by subtracting an output value of the integrating process, applied to a finite response low-pass filter process, from the output value.

A CPU substitutes a difference between a crank-side speed that is a rotation speed of a crankshaft and a downstream-side speed that is a speed of a portion, opposite from the crankshaft, in a damper into a differential speed. The CPU calculates a torsion speed component that is a speed component of the crankshaft due to torsion of the damper based on a physical model of which an input is the differential speed, and calculates a time that is a variable indicating a speed of the crankshaft, used to determine a misfire, based on the torsion speed component. The CPU delays acquisition time of the downstream-side speed used to calculate the differential speed, with respect to acquisition time of the crank-side speed according to the rotation speed of the crankshaft.

An air feed ratio controlling apparatus can include a predictor for predicting an air fuel ratio on the downstream side of a catalyst calculates a predicted air fuel ratio at least based on an actual air fuel ratio from an oxygen sensor and a history of a first correction coefficient. The air fuel ratio controlling apparatus can also include an adaptive model corrector which determines the deviation between the actual air fuel ratio and the predicted air fuel ratio as a prediction error ERPRE, and superposes a second correction coefficient on the first correction coefficient so that the prediction error may be reduced to zero.

A system to predict calibration values for a vehicle. The system is configured to receive a plurality of training data sets for a component of the vehicle. Each of the plurality of training data sets includes one or more training inputs and one or more corresponding training outputs. The system is further configured to automatically develop a prediction model based on the plurality of training data sets. The system is further configured to receive an input data set and determine, using the prediction model, a predicted calibration value based on the input data set. The system is further configured to transmit the predicted calibration value to an electronic control unit of the vehicle.

A system according to the principles of the present disclosure includes a first model predictive control (MPC) module and an engine actuator module. The first MPC module generates predicted parameters based on a model of an engine and a set of possible target values and generates a cost for the set of possible target values based on the predicted parameters and a desired exhaust enthalpy. The first MPC module also selects the set of possible target values from multiple sets of possible target values based on the cost. The engine actuator module adjusts an actuator of the engine based on at least one of the target values.

The speed of a turbocharger may be estimated using data from sensors that are readily available in most engine management systems. In some cases, a pressure measurement from a MAP sensor may be used, in combination with one or more computational models, to provide an efficient, lower cost estimate of turbo speed that can be used to control operation of the engine and/or the turbocharger.

An engine system includes: a first throttle valve; a turbocharger compressor disposed downstream of the first throttle valve; a charge air cooler disposed downstream of the turbocharger compressor; a second throttle valve located downstream of the turbocharger compressor; a purge inlet located downstream of the first throttle valve and configured to introduce fuel vapor from a fuel tank into intake air; and an engine control module configured to: maintain the first throttle valve in a fully open position; and selectively close the first throttle valve relative to the fully open position in response to receipt of a request to at least one of: purge fuel vapor from the fuel tank; and at least one of decrease and prevent icing of the charge air cooler.

Systems and methods automatically rescale an original engine model so that it models an engine of a different size. The original engine model may be coupled to an engine controller model, and the systems and methods may also rescale the original controller model to produce a rescaled controller model matched to the rescaled engine model. The original engine model may include engine parameters and engine lookup tables, and the original controller model may include controller parameters and controller lookup tables. Rescaling factors indicating the size of the new engine being modeled may be received, and ratios may be computed as a function of the rescaling factors. Original engine parameters and controller parameters may be rescaled based on the ratios. Original engine lookup tables and controller lookup tables may be reshaped based on the ratios.

A control apparatus for the internal combustion engine has a multicore processor mounted with a plurality of the cores, calculates various tasks regarding an operation of the internal combustion engine, and distributes the tasks to the plurality of the cores respectively to perform a calculation, and a controller that makes the number of cores for use in the calculation smaller while fuel cutoff is carried out than before the fuel cutoff is carried out. The controller selects, as a designated core, at least one of the cores for use in a specific calculation associated with combustion of the internal combustion engine. The controller stops the designated core from being used while fuel cutoff is carried out. As the specific calculation associated with combustion, for example, it is possible to mention a combustion forecasting calculation of a cylinder model, a temperature forecasting calculation of a catalyst temperature estimation model, and a fuel adhesion amount forecasting calculation of a fuel adhesion model.