A catalyst state estimation apparatus includes a first sensor, a memory and a processor. The first sensor is configured to acquire information about a catalyst that removes a toxic substance in an exhaust gas, the first sensor being provided in a main passage into which the exhaust gas flows from an internal combustion engine. The memory is configured to previously store a catalyst state estimation model that includes at least one mathematical model. The processor is configured to estimate a removal performance of the catalyst by applying the information about the catalyst acquired by the first sensor to the catalyst state estimation model.

The subject matter of the present invention relates to trained machine-learning models (300), methods (200, 400) and apparatuses (500) allowing a future resonant frequency of a catalyst for selective reduction of nitrogen oxides (SCR) to be predicted, the resonant frequency being representative of a concentration of a reducing agent within the SCR. The SCR forms part of a system for after-treatment of a flow of exhaust gases of an internal combustion engine with which a motor vehicle is provided. The general principle of the invention is based on the observation of correlations between the resonant frequency of an SCR and the concentration of ammonia present within the SCR. This observation led the inventor to envision using machine learning to create a trained machine-learning model in order to predict the resonant frequency of an SCR. In the invention, the trained machine-learning model is a so-called predictive model in which significant correlations are discovered in a set of past observations and in which it is sought to generalize these correlations to cases that have not yet been observed.

A method for operating an exhaust after-treatment system including an SCR-catalyst, a metering device for dosing a reducing agent being controlled on the basis of a determining variable that influences a nitrogen-oxide concentration downstream of the SCR-catalyst. A breakthrough identification is carried out for the SCR-catalyst, wherein if a breakthrough is identified, the determining variable is altered to a higher nitrogen-oxide concentration downstream of the SCR-catalyst and the metering device is controlled in order to dose the reducing agent on the basis of the altered determining variable.

Vehicle exhaust system uses delta pressure based estimation of accumulated soot within a diesel particulate filter. The exhaust system has a diesel oxidation catalyst and a diesel particulate filter. A fuel injector is connected upstream from the diesel oxidation catalyst and the diesel particulate filter. A delta pressure sensor measures difference in pressure at inlet and outlet of the diesel particulate filter. A controller determines when to regenerate the diesel particulate filter based on an estimated amount of soot. The controller, in a first regeneration mode, causes the fuel injector to inject fuel at a first rate into the exhaust stream, and to re-evaluate amount of soot accumulated within the diesel particulate filter under increased volumetric flow. The controller, in a second regeneration mode, causes the fuel injector to inject fuel at a second rate into the exhaust stream in order to combust soot trapped in the diesel particulate filter.

A system may include a NO.sub.x sensor and a controller. The controller may be configured to interpret a value of a first parameter indicative of an amount of NO.sub.x measured by the NO.sub.x sensor and interpret a value of a second parameter for a NO.sub.x value from a look-up table. The controller may be further configured to determine a correction factor based on the value of the first parameter and the value of the second parameter and generate a dosing command based, at least in part, on the determined correction factor. In some implementations, the NO.sub.x value from the look-up table may be based on one or more operating conditions of an engine. In some implementations, the controller may be further configured to update a NO.sub.x value of the look-up table based on the determined correction factor.

A system for control of an internal combustion system having subsystems, each with different response times. Subsystems may include a fuel system, an air handling system, and an aftertreatment system, each being operated in response to a set of reference values generated by a respective target determiner. Calibration of each subsystem may be performed independently. The fuel system is controlled at a first time constant. The air handling system is controlled on the order of a second time constant slower than the first time constant. The aftertreatment system is controlled on the order of a third time constant slower than the second time constant. A subsystem manager is optionally in operative communication with each target determiner to coordinate control. Generally, dynamic parameters from slower subsystems are treated as static parameters when determining reference values for controlling a faster subsystem.

A catalyst temperature estimation device that estimates a temperature of a catalyst provided in an exhaust passage of an internal combustion engine includes a storage device and processing circuitry. The storage device stores mapping data that specifies a mapping that uses multiple input variables to output an estimation value of the temperature of the catalyst. The multiple input variables include at least one variable of an ambient temperature variable or an excess amount variable. The multiple input variables further include a fluid energy variable, which is a state variable related to energy of fluid flowing into the catalyst, and a previous cycle value of the estimation value of the temperature of the catalyst. The processing circuitry is configured to execute an acquisition process, a temperature calculation process, and an operation process. The mapping data includes data that is learned through machine learning.

A catalyst deterioration detection device is provided to detect deterioration of a catalyst provided in an exhaust passage of an internal combustion engine. The catalyst deterioration detection device includes a storage device and processing circuitry. The storage device stores map data specifying a mapping that uses time series data of an excess amount variable in a first predetermined period and time series data of a downstream detection variable in a second predetermined period as inputs to output a deterioration level variable. The processing circuitry executes an acquisition process that acquires data, a deterioration level variable calculation process that calculates a deterioration level variable of the catalyst based on an output of the mapping using the data acquired by the acquisition process as an input. The map data includes data that is learned through machine learning.

A method for ascertaining emissions of a motor vehicle driven with the aid of an internal combustion engine in a practical driving operation. A machine learning system is trained to generate time curves of the operating variables with the aid of measured time curves of operating variables of the motor vehicle and/or of the internal combustion engine, and to then ascertain the emissions as a function of these generated time curves.

A method is proposed for regulating a fill level of an exhaust component storage of a catalyst (26) of an internal combustion engine (10), wherein the regulating of the fill level is done by using a system model (100), comprising a catalyst model (102), and wherein uncertainties of measured or model variables influencing the regulating of the fill level are corrected by an adaptation, which is based on signals of an exhaust gas probe (34) arranged at the outlet side of the catalyst (26). The method is characterized in that the adaptation takes multiple pathways (200, 210, 220), wherein signals from different signal regions (260, 280, 300) of the exhaust gas probe (34) situated at the outlet side are processed on different pathways. An independent claim is addressed to a controller designed to carry out the method.