A fuel management system includes a memory and a control module. The memory stores fuel rate maps for multiple firing fractions, where: each of the firing fractions corresponds to a respective firing pattern of an engine; at least some of the firing patterns include deactivating one or more cylinders. The control module: for each of the firing fractions, determines a fuel efficiency value for each of multiple transmission gear ratios, where fuel efficiency values are provided for transmission ratio and firing fraction pairs; applies drive ability constraints to provide resultant transmission ratio and firing fraction pairs; subsequent to applying the drive ability constraints and based on the fuel efficiency values, selects one of the resultant transmission ratio and firing fraction pairs; and concurrently operates a transmission and the engine according to the selected one of the transmission ratio and firing fraction pairs.

An engine and one or more aftertreatment subsystems integrated into one system for optimization and control. At least one controller may be connected to the engine and the one or more aftertreatment subsystems. The controller may contain and execute a program for the optimization and control of the one system. Controller may receive information pertinent to the engine and the one or more aftertreatment subsystems for the program. The controller may prescribe setpoints and constraints for measured variables and positions of actuators according to the program to aid in effecting the optimization and control of the one system.

Systems and methods for operating a driveline of a hybrid vehicle are disclosed. In one example, output of an electric machine is adjusted after commanding reactivation of all engine cylinder valves that have been deactivated. The electric machine torque may counteract the engine producing torque that is greater than a requested torque due to high intake manifold pressure.

A method of controlling an electronically controllable valve of an engine includes receiving, from one or more operation sensors, operation data including sensor data corresponding to a condition of the engine, control inputs indicative of operation of equipment that includes the engine, or a combination thereof. The method includes determining, using a trained valve control model, an operating characteristic of the valve at least partially based on the operation data, and generating a control signal to effect operation of the valve in accordance with the operating characteristic.

A method for a model-based open-loop and closed-loop control of an internal combustion engine includes the steps of: calculating, by an optimizer, a pre-optimized quality measure based on an operating situation, wherein, in calculating the pre-optimized quality measure, a plurality of discrete manipulated variables having a plurality of discrete settings are interpreted as a plurality of continuous manipulated variables having a continuous settings range; quantizing the plurality of continuous manipulated variables, and the plurality of continuous manipulated variables are set as a plurality of new discrete manipulated variables (SG(new)) having a plurality of discrete settings; and calculating, by the optimizer, a post-optimized quality measure based on the plurality of new discrete manipulated variables and the operating situation of the internal combustion engine, and the post-optimized quality measure is set as critical for an operating point of the internal combustion engine by the optimizer.

A control device includes a controller configured to: obtain a driving condition and an actual value of a controlled amount of an engine; use a transformed engine model to calculate an estimate value of a future controlled amount, from the obtained driving condition and a candidate value of an operation amount for controlling the controlled amount, the transformed engine model in which an activation function of each neuron is transformed using a linear inequality function having a binary variable, the transformed engine model for which a weight coefficient, bias, and upper and lower limits of an input/output variable of the each neuron are set based on an engine model that has a multilayer neural structure and the activation function of a ReLU structure, receives the driving condition and operation amount as input, and predicts dynamic characteristics of the engine; determine a value of the operation amount, based on error between the calculated estimate value and a target value of the controlled amount identified from the driving condition.

A method and a system for controlling a propulsive power output applied to a propeller shaft of a ship. The ship includes the propeller shaft and a propulsive power source connected to the propeller shaft. A control signal for producing with the propulsive power source a propulsive power is varied within an interval limited by an upper control limit value and a lower control limit value. If a current value of an operational parameter of the ship reaches a first parameter limit value, the upper control limit value is reduced. Thus, the propulsive power source may be prevented from applying a too high power output to the propeller shaft, which would be unfavourable for the ship.

Provided is a method of generating vehicle control data. The method is applied to a vehicle configured to select one of a plurality of traveling control modes and is executed by a processor in a state in which relationship definition data defining a relationship between a state of the vehicle and an action variable as a variable relating to an operation of electronic equipment in the vehicle is stored in a memory. The method includes operation processing for operating the electronic equipment, acquisition processing for acquiring a detection value of a sensor configured to detect the state of the vehicle, reward calculation processing for providing reward, and update processing for updating the relationship definition data.

A control device includes a controller configured to: obtain a driving condition and an actual value of a controlled amount of an engine; use a transformed engine model to calculate an estimate value of a future controlled amount, from the obtained driving condition and a candidate value of an operation amount for controlling the controlled amount, the transformed engine model in which an activation function of each neuron is transformed using a linear inequality function having a binary variable, the transformed engine model for which a weight coefficient, bias, and upper and lower limits of an input/output variable of the each neuron are set based on an engine model that has a multilayer neural structure and the activation function of a ReLU structure, receives the driving condition and operation amount as input, and predicts dynamic characteristics of the engine; determine a value of the operation amount, based on error between the calculated estimate value and a target value of the controlled amount identified from the driving condition.

Various aspects of the present disclosure are directed to, for example, a method for calculating a data envelope while calibrating a technical system. In some specific embodiments, the d-dimensional calibration space, which comprises the calibration variables required for the calibration, is divided into a first sub-calibration space having a dimension d.sub.sub<d and at least one further sub-calibration space, and a d.sub.sub-dimensional data envelope is calculated at least for the first sub-calibration space using available data points and is checked during the calibration as an auxiliary condition.