A method for requirement-based servicing of an injector in a common-rail system in which, during ongoing operation of the engine, a current operating point is stored as a function of the rail pressure and of the fuel injection mass, and the current operating point is multiplied by a damage factor and is stored as a reference injection cycle as a function of the rail pressure as well as of the fuel injection mass. A total reference injection cycle is calculated by forming sums over the reference injection cycles, and a load factor is calculated as a function of the total reference injection cycle and the permissible injection cycles, and the load factor is set as decisive for the servicing recommendation of the injector.

First ECUs and second ECU of a control system of an internal combustion engine is configured to: output a predicted value of an output parameter by using a learning model if actually measured values of input parameters are input; and control an internal combustion engine based on this predicted value, the first ECUs is configured to: learn a learning model; and transmit first vehicle information including a usage environment and usage state of the first vehicle and the learned learning model linked with each other, and the second ECU is configured to receive the learned learning models, and wherein the second ECU is configured to use a learned learning model linked with the first vehicle information closest to the usage environment and usage state of the second vehicle.

A control device of an internal combustion engine includes: a parameter value acquiring part acquiring values of input parameters; a computing part utilizing a model using a neural network to calculate a value of an output parameter, and a control part controlling operation of the engine. The model outputs the value of the output parameter from an output layer node if the values of the input parameters are input to the input layer nodes. When an abnormality occurs at values of part of the input parameters among the input parameters, the computing part uses the corrected model to calculate the value of the output parameter, the corrected model being provided by correcting the model so that a value changing in accordance with a value of an abnormal input parameter is not input from a input layer node corresponding to the abnormal input parameter to a hidden layer node.

An engine system includes an air handling control unit which controls a plurality of air handling actuators responsible for maintaining flow of air and exhaust gas within the engine system. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The air handling control unit includes a controller which controls the air handling actuators of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines a control signal to send to the controller by using the policy function after receiving the current state as an input, and outputs the control signal to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value. Subsequently, the controller controls the air handling actuators in response to receiving the control signal. In one aspect of the embodiment, the control signal is a command signal for the air handling actuators.

A control device of an internal combustion engine can respond to the variation of the flow reduction rate attributable to the difference in a clogging condition, and which can detect a deposit accumulation amount even if no idle condition is provided like HEV, etc. In a control device of an internal combustion engine, a map correction section determines a correction amount in accordance with an approximate line result based on an air amount at a predetermined opening degree at which the measurement of the air amount by an air flow meter is sufficiently performed with the exception of a first predetermined opening degree in the case where the measurement of the air amount by the air flow meter has not been performed sufficiently in the first predetermined opening degree in a current operation cycle.

An internal combustion engine controller comprising a memory and a processor is provided. The memory is configured to store a plurality of control maps, each control map defining a hypersurface of actuator setpoints for controlling an actuator of the internal combustion engine based on a plurality of input variables to the internal combustion engine controller. The processor comprises an engine setpoint module and a map updating module. The engine setpoint module is configured to output a control signal to each actuator based on a location on the hypersurface of the respective control map defined by the plurality of input variables. The map updating module is configured to calculate an optimised hypersurface for at least one of the control maps. The optimised hypersurface is calculated based on a real-time performance model of the internal combustion engine comprising sensor data from the internal combustion engine and the plurality of input variables. The map updating module further is configured to update the hypersurface of the control map based on the optimised hypersurface. A method of controlling an internal combustion engine is also provided.

An internal combustion engine controller for an internal combustion engine comprising a memory and a processor. The memory is configured to store a plurality of control maps, each control map defining a hypersurface of actuator setpoints for controlling an actuator of the internal combustion engine based on a plurality of input variables to the internal combustion engine controller. The processor comprises a map updating module, a parameter updating module and an engine setpoint module. The map updating module is configured to calculate an optimised hypersurface for at least one of the control maps based on a performance objective function of the internal combustion engine, sensor data from the internal combustion engine, and the plurality of input variables, wherein the performance objective function includes parameters. The parameter updating module is configured to update a parameter of the performance objective function upon determining a change in an operating condition of the internal combustion engine. The parameters comprise one or both of: engine parameters associated with an engine model; and cost parameters associated with a cost function. The map updating module is further configured to update the hypersurface of the control map based on the optimised hypersurface. The engine setpoint module is configured to output a control signal to each actuator based on a location on the hypersurface of the respective control map defined by the plurality of input variables.

The subject matter of this specification can be embodied in, among other things, a method for characterizing a fluid injector that includes receiving a collection of waveform data, identifying a pull locus, determining a detection threshold level value, identifying a first subset of the collection of data representative of a selected first electrical waveform of the collection of electrical waveforms, identifying an opening value, identifying a representative closing value, identifying an anchor value, identifying a second subset of the collection of data based on the collection of data, the pull locus, the first subset, and the opening value, identifying a maximum electrical value, identifying an opening locus based the collection of data, the anchor value, and the maximum electrical value, identifying a hold value, and providing characteristics associated with the fluid injector comprising the pull locus, the opening locus, the hold value, the anchor value, and the representative closing value.

Race car settings (e.g., Formula 1 engine mix settings) are developed for particular racing goals such as faster lap time, better acceleration, less vehicle wear, etc., using pattern templates that are derived from historical racing scenarios. The historical scenarios provide data on racing settings, racing results, and racing conditions such as squad information, equipment information, and environmental information. A cognitive (deep question answering) system can select an initial pattern template based on current racing conditions, and present suggested vehicle settings to the user (driver) using the initial pattern template. The driver can select from different candidate values for various factors, which may lead to the presentation of additional suggestions or the use of additional pattern templates. The final settings map is created based on the employed pattern templates and the driver selections.

A method of operating a fuel injector of an internal combustion engine includes setting a value of a target fuel quantity to be injected by the fuel injector, initializing a value of a fuel quantity requested from the fuel injector to the value of the target fuel quantity, and correcting the value of the requested fuel quantity. A first learning cycle is performed to correct the value of the requested fuel quantity in which a difference between the target fuel quantity and the injected fuel quantity is calculated and added to the requested fuel quantity to provide a corrected value. The corrected value of the requested fuel quantity is used to determine a reference value of an energizing time that causes the fuel injector to inject a fuel quantity corresponding to the target fuel quantity. The fuel injector is operated based on the determined reference value of the energizing time.