Abstract: Provided is a processor-implemented method and a processor in a vehicle for estimating the value of a quantity for which a physical sensor is not available for measurement. The method includes: receiving a plurality of measured signals representing values of measurable variables; computing, in real-time, time derivatives of the measured signals; and applying a trained feedforward neural network, in real-time, to estimate values for a plurality of unmeasurable variables, the unmeasurable variables being variables that are unmeasurable in real-time, the feedforward neural network having been trained using test data containing time derivatives of values for the measurable variables and values for the unmeasurable variables; wherein the vehicle uses the estimated values for the unmeasurable variables for vehicle operation.

Abstract: A system or method for determining virtual data of a system, relative to a measurement point having a sensor located nearby, is determined by a controller. The system calculates modeled data at the measurement point, filters the modeled data to determine filtered data, and calculates a differential between the modeled data and the filtered data to determine a compensation term. The system also determines raw-sensed data from the sensor at the measurement point, and combines that raw-sensed data with the compensation data to calculate the virtual data at the measurement point. In some configurations, the modeled data is determined from a physics-based model. Furthermore, filtering the modeled data may include using a low-pass filter, and a time constant for the low-pass filter may be calculated based on operating conditions of the system.

Abstract: A control module includes a dynamic target selection module configured to receive an intake manifold pressure setpoint and a measured intake manifold pressure, select between the intake manifold pressure setpoint and the measured intake manifold pressure, and output a selected intake manifold pressure setpoint based on the selection. A multivariable control module is configured to receive at least one target setpoint that is based on the selected intake manifold pressure setpoint and control operation of an air charging system of a vehicle based on the at least one target setpoint.

Abstract: A control module includes a dynamic target selection module configured to receive an intake manifold pressure setpoint and a measured intake manifold pressure, select between the intake manifold pressure setpoint and the measured intake manifold pressure, and output a selected intake manifold pressure setpoint based on the selection. A multivariable control module is configured to receive at least one target setpoint that is based on the selected intake manifold pressure setpoint and control operation of an air charging system of a vehicle based on the at least one target setpoint.

Abstract: An emissions control system for a motor vehicle that includes an internal combustion engine includes a first selective catalytic reduction (SCR) device and a reductant injector, The system further includes a model-based controller that is configured to calculate a target amount of reductant to inject to maintain a predetermined ratio between an amount of NH3 and an amount of NOx at the outlet of the first SCR device, and to send a command for receipt by the reductant injector to inject the calculated amount of reductant. The model-based controller is further configured to send a command for receipt by an engine controller to influence NOx production by the engine by modifying an engine operating parameter, based on a calculated target amount of NOx at the inlet of the first SCR device.

Abstract: Provided is a processor-implemented method and a processor in a vehicle for estimating the value of a quantity for which a physical sensor is not available for measurement. The method includes: receiving a plurality of measured signals representing values of measurable variables; computing, in real-time, time derivatives of the measured signals; and applying a trained feedforward neural network, in real-time, to estimate values for a plurality of unmeasurable variables, the unmeasurable variables being variables that are unmeasurable in real-time, the feedforward neural network having been trained using test data containing time derivatives of values for the measurable variables and values for the unmeasurable variables; wherein the vehicle uses the estimated values for the unmeasurable variables for vehicle operation.

Abstract: A system or method for determining virtual data of a system, relative to a measurement point having a sensor located nearby, is determined by a controller. The system calculates modeled data at the measurement point, filters the modeled data to determine filtered data, and calculates a differential between the modeled data and the filtered data to determine a compensation term. The system also determines raw-sensed data from the sensor at the measurement point, and combines that raw-sensed data with the compensation data to calculate the virtual data at the measurement point. In some configurations, the modeled data is determined from a physics-based model. Furthermore, filtering the modeled data may include using a low-pass filter, and a time constant for the low-pass filter may be calculated based on operating conditions of the system.

Abstract: An emissions control system for a motor vehicle that includes an internal combustion engine includes a first selective catalytic reduction (SCR) device and a reductant injector, The system further includes a model-based controller that is configured to calculate a target amount of reductant to inject to maintain a predetermined ratio between an amount of NH3 and an amount of NOx at the outlet of the first SCR device, and to send a command for receipt by the reductant injector to inject the calculated amount of reductant. The model-based controller is further configured to send a command for receipt by an engine controller to influence NOx production by the engine by modifying an engine operating parameter, based on a calculated target amount of NOx at the inlet of the first SCR device.

Abstract: A torque requesting module generates a torque request for an engine based on driver input. A model predictive control (MPC) module: identifies sets of possible target values based on the torque request, each of the sets of possible target values including target effective throttle area percentage; determines predicted operating parameters for the sets of possible target values, respectively; determines cost values for the sets of possible target values, respectively; selects one of the sets of possible target values based on the cost values; and sets target values based on the possible target values of the selected one of the sets, respectively, the target values including a target pressure ratio across the throttle valve. A target area module determines a target opening area of the throttle valve based on the target effective throttle area percentage ratio. A throttle actuator module controls the throttle valve based on the target opening.

Abstract: Embodiments of the present invention described herein include a control system for a vehicle. The control system includes a control module that dynamically computes a first power value (PC) to be generated by a compressor based on an estimation model of the compressor. The control system further includes a power term estimator module that dynamically computes a second power value (PU) based on a measured compressor pressure ratio (?cmeas) of the compressor. The control module further computes an amount of power to be generated by an engine (Ptdes) by adding the first power value and the second power value. The control module further adjusts an actuator position to generate the amount of power to be generated.

Abstract: Embodiments of the present invention described herein include a control system for a vehicle. The control system includes a control module that dynamically computes a first power value (PC) to be generated by a compressor based on an estimation model of the compressor. The control system further includes a power term estimator module that dynamically computes a second power value (PU) based on a measured compressor pressure ratio (?cmeas) of the compressor. The control module further computes an amount of power to be generated by an engine (Ptdes) by adding the first power value and the second power value. The control module further adjusts an actuator position to generate the amount of power to be generated.

Abstract: A prediction module is configured to, based on a set of possible target values for future times, determine predicted efficiency values for set of possible target values at the future times, respectively. A cost module is configured to determine a cost for the set of possible target values based on comparisons of the predicted efficiency values and a reference efficiency value. A selection module is configured to: (i) based on the cost of the set of possible target values, select the set of possible target values from a group including: the set of possible target values; and N other sets of possible target values; and (ii) set target values to respective ones of the selected set of possible target values. A first valve control module is configured to actuate a first coolant valve based on a first one of the target values.

Abstract: A method of controlling the operation of an air charging system is disclosed. A plurality of output parameters of the air charging system are monitored. An error between each one of the monitored output parameters and a target value thereof is calculated. Each one of the calculated errors is applied to a linear controller that yields a virtual input which is used to calculate a plurality of input parameters for the air charging system. Each one of the input parameters is used to determine the position of a corresponding actuator of the air charging system and operate of the actuators according to the determined position thereof. The inputs parameters are calculated with a non-linear mathematical model of the air charging system configured such that each one of the virtual inputs is in a linear relation with only one of the output parameters.

Abstract: A method is disclosed for controlling an injector for injecting a reductant into a selective catalytic reduction system of an internal combustion engine. A value of a concentration of nitrogen-oxides in the exhaust gas aftertreatment system downstream of the selective catalytic reduction system is measured, and a first difference is calculated between the measured value of the nitrogen-oxides concentration and a predetermined reference value thereof. A value of a concentration of ammonia in the exhaust gas aftertreatment system downstream of the selective catalytic reduction system is measured, and a second difference is calculated between the measured value of the ammonia concentration and a predetermined reference value thereof. A quantity of reductant to be injected by the injector is calculated as a function of the calculated first difference and second difference, and the injector is operated to inject the calculated quantity of reductant.

Abstract: A method and apparatus is disclosed to control the operation of an air charging system of an internal combustion engine. A plurality of output parameters of the air charging system are monitored. An error is calculated between the monitored output parameters and a target value thereof. The calculated errors are applied to a linear controller that yields a virtual input used to calculate a plurality of input parameters for the air charging system. The input parameters is used to determine the position of a corresponding actuator of the air charging system for operating the actuators according to the determined position thereof. The inputs parameters are calculated with a non-linear mathematical model of the air charging system configured such that the virtual inputs are in a linear relation with only one of the output parameters and vice versa.

Abstract: Disclosed are model predictive control (MPC) systems, methods for using such MPC systems, and motor vehicles with selective catalytic reduction (SCR) employing MPC control. An SCR-regulating MPC control system is disclosed that includes an NOx sensor for detecting nitrogen oxide (NOx) input received by the SCR system, catalyst NOx sensors for detecting NOx output for two SCR catalysts, and catalyst NH3 sensors for detecting ammonia (NH3) slip for each SCR catalyst. The MPC system also includes a control unit programmed to: receive desired can conversion efficiencies for the SCR catalysts; determine desired can NOx outputs for the SCR catalysts; determine maximum NH3 storage capacities for the SCR catalyst; calculate the current can conversion efficiency for each SCR catalyst; calculate an optimized reductant pulse-width and/or volume from the current can conversion efficiencies; and, command an SCR dosing injector to inject a reductant into an SCR conduit based on the calculated pulse-width/volume.

Abstract: An automotive vehicle includes an internal combustion engine and an exhaust system. The exhaust treatment system includes a dosing system that injects NH3 into an exhaust gas stream generated by the engine. An SCR device stores an amount of the NH3 and converts NOx into diatomic nitrogen (N2) and water (H2O) based on the stored amount of the NH3. The vehicle further includes an SCR status estimator device and a controller. The SCR status estimator device determines an NH3 coverage ratio (R), which indicates a stored amount of NH3 with respect to a maximum NH3 storage capacity of the SCR device. The controller determines a target NOx reduction efficiency (?NOx) of the SCR device, and an NH3 coverage ratio set point (Rsp) based on the ?NOx. The controller also generates an NH3 control signal (u) that controls the dosing system based on a comparison between the R and the Rsp.

Abstract: An automotive vehicle includes an internal combustion engine and an exhaust system. The exhaust treatment system includes a dosing system that injects NH3 into an exhaust gas stream generated by the engine. An SCR device stores an amount of the NH3 and converts NOx into diatomic nitrogen (N2) and water (H2O) based on the stored amount of the NH3. The vehicle further includes an SCR status estimator device and a controller. The SCR status estimator device determines an NH3 coverage ratio (R), which indicates a stored amount of NH3 with respect to a maximum NH3 storage capacity of the SCR device. The controller determines a target NOx reduction efficiency (?NOx) of the SCR device, and an NH3 coverage ratio set point (Rsp) based on the ?NOx. The controller also generates an NH3 control signal (u) that controls the dosing system based on a comparison between the R and the Rsp.

Abstract: A torque requesting module generates a torque request for an engine based on driver input. A model predictive control (MPC) module: identifies sets of possible target values based on the torque request, each of the sets of possible target values including target effective throttle area percentage; determines predicted operating parameters for the sets of possible target values, respectively; determines cost values for the sets of possible target values, respectively; selects one of the sets of possible target values based on the cost values; and sets target values based on the possible target values of the selected one of the sets, respectively, the target values including a target pressure ratio across the throttle valve. A target area module determines a target opening area of the throttle valve based on the target effective throttle area percentage ratio. A throttle actuator module controls the throttle valve based on the target opening.

Abstract: Disclosed are model predictive control (MPC) systems, methods for using such MPC systems, and motor vehicles with selective catalytic reduction (SCR) employing MPC control. An SCR-regulating MPC control system is disclosed that includes an NOx sensor for detecting nitrogen oxide (NOx) input received by the SCR system, catalyst NOx sensors for detecting NOx output for two SCR catalysts, and catalyst NH3 sensors for detecting ammonia (NH3) slip for each SCR catalyst. The MPC system also includes a control unit programmed to: receive desired can conversion efficiencies for the SCR catalysts; determine desired can NOx outputs for the SCR catalysts; determine maximum NH3 storage capacities for the SCR catalyst; calculate the current can conversion efficiency for each SCR catalyst; calculate an optimized reductant pulse-width and/or volume from the current can conversion efficiencies; and, command an SCR dosing injector to inject a reductant into an SCR conduit based on the calculated pulse-width/volume.