Systems, devices and methods for diagnosing malfunctioning in a crankcase ventilation (CCV) system can include a controller receiving a plurality of pressure values. The plurality of pressure values include a first pressure value indicative of pressure of gases flowing between a crankcase and a breather assembly, a second pressure value indicative of pressure of gases flowing through a CCV tube from the breather assembly, and a third pressure value representing pressure of gases in a tube connected to the CCV tube. The controller can calculate a pair of pressure differences including a first pressure difference between the first pressure value and the second pressure value and a second pressure difference between the first pressure value and the third pressure value. The controller can detect a malfunctioning or defect in the CCV system based on the pair of pressure differences falling within a predefined clustering region.

A combustion control method and system for an engine of a vehicle comprises a controller configured to access a trained feedforward artificial neural network configured to model a volumetric efficiency (VE) of the engine based on measured engine speed, engine intake manifold absolute pressure, intake and exhaust camshaft positions, intake air temperature, and engine coolant temperature, generate a base VE of the engine using the trained feedforward artificial neural network and the measured parameters, estimate an air charge mass flowing to each cylinder of the engine based on the base VE of the engine, and control combustion in the cylinders of the engine based on the estimated air charge mass to improve at least one of combustion stability, torque response, and fuel economy.

A vehicle control system is provided. A classification process classifies vehicles into groups based on information related to vehicles. In order to update relationship defining data for each of the classified groups, an update process inputs, into an update map, states of the vehicles belonging to a same group, values of action variables used to operate the electronic devices of the vehicles belonging to the same group, and rewards corresponding to the operation of the electronic devices.

An engine optimization controller may determine values of a set of input parameters of an engine and process, using an engine model, the values to determine a plurality of sets of potential output parameters. The engine optimization controller may determine, based on the plurality of sets of potential output parameters, an engine optimization scheme, which may indicate a first number of cylinders, of one or more cylinders of the engine, to be active and to receive gas; a second number of cylinders, of the one or more cylinders, to be inactive and to receive gas, and/or a third number of cylinders, of the one or more cylinders, to be inactive and to not receive gas. The engine optimization controller may provide the engine configuration scheme to another controller to allow control of the one or more cylinders and one or more fuel injectors according to the engine configuration scheme.

An engine controller includes an ignition timing control unit and a rich imbalance determining unit. The rich imbalance determining unit designates one of multiple cylinders as a subject cylinder for determination and executes lean active control that commands a smaller amount of fuel injection for the subject cylinder than for the other cylinders. The rich imbalance determining unit determines whether an air-fuel ratio of the subject cylinder deviates to be richer based on a rotational fluctuation amount of a crankshaft during the execution of the lean active control. The ignition timing control unit executes an advancement limiting process that limits advancement of the ignition timing by the knock control during the execution of the lean active control.

A state estimation device for an internal combustion engine includes: a storage device that stores mapping data, the mapping data being data defining a mapping that takes as an input an internal combustion engine state variable and that generates as an output an estimated value for estimating the state of the internal combustion engine; and an execution device that executes an acquisition process of acquiring the internal combustion engine state variable and an estimation process of calculating the estimated value based on the output of the mapping. The mapping data is data learned by machine learning. When the estimated value is out of an acceptable range, the execution device executes a guard process of adjusting the estimated value to a value close to or within the acceptable range. When executing the guard process, the execution device calculates the value after the guard process as the estimated value.

A method of continuously variable valve duration (CVVD) location learning may include when a controller determines necessity of position learning for short duration and long duration of a CVVD system, performing conditional application re-learning control in which the position learning is performed in a situation in which validity determination of system environment condition for CVVD hardware and validity determination of vehicle environment condition for engine operation information of an engine are satisfied.

According to one embodiment, a machine control system includes: a selecting unit which acquires a state quantity of a machine converted from data acquired by a sensor provided in the machine to select two or more learning models according to the acquired state quantity; a composing unit which inputs the state quantity acquired by the selecting unit to each of the two or more learning models selected by the selecting unit to calculate a composed value using a command value output from each of the learning models; and a learning unit which outputs a command value with respect to the machine in a range based on the composed value calculated by the composing unit, acquires a state quantity of the machine, searches for a command value of a specific condition from a combination of the output command value and the acquired state quantity, and outputs the searched command value to the machine, thereby creating a new learning model.

A vehicle controller is provided. An update process updates a relationship defining data by inputting, to a predetermined update map, a state of a vehicle, a value of an action variable used to operate an electronic device, and a reward corresponding to that electronic device. A reward calculating process provides, based on the state of the vehicle obtained by an obtaining process, a greater reward when a characteristic of the vehicle meets a standard than when the characteristic of the vehicle does not meet the standard. A loosening process loosens the standard to a larger extent when the degree of deterioration is large than when the degree of deterioration is small.

A vehicle controller is provided. An update process updates a relationship defining data by inputting, to a predetermined update map, a state of a vehicle, a value of an action variable used to operate an electronic device, and a reward corresponding to that electronic device. A reward calculating process provides, based on the state of the vehicle obtained by an obtaining process, a greater reward when a characteristic of the vehicle meets a standard than when the characteristic of the vehicle does not meet the standard. A loosening process loosens the standard to a larger extent when the degree of deterioration is large than when the degree of deterioration is small.