Gm Global Technolgoy Operations, Inc.

Other

Daniele Bernardini

Alberto Bemporad

Christopher E Whitney

Michael Livshiz

Julian R Verdejo

Kevin C Wong

Gary Robert Cygan, Jr

Ning Jin

Ruixing Long

Bharath Pattipati

Giuseppe Mazzara Bologna

Jyh-shin Chen

Vincenzo Alfieri

Giuseppe Conte

Alessia Tuninetti

Alessandra Neri

Giulio Binetti

Maria Camuglia

Technical solutions are described for selective catalytic reduction diagnosis in an emissions control system or an exhaust system used for treating exhaust gas, such as in a motor vehicle including an internal combustion engine. An example emissions control system includes a selective catalytic reduction (SCR) device. The emissions control system further includes a controller that is performs SCR fault diagnosis. The SCR fault diagnosis includes computing a plurality of estimated NH3 storage capacity values (&#x398;) of an SCR catalyst of the SCR device. The diagnosis further includes determining a diagnostic parameter of the SCR device using the plurality of estimated NH3 storage capacity values.

Selective catalytic reduction fault detection

A propulsion system, control system, and method are provided for optimizing fuel economy, which use model predictive control systems to generate a plurality of sets of possible command values and determine a cost for each set of possible command values of based on a first predetermined weighting value, a second predetermined weighting value, a plurality of predicted values, and a plurality of requested values. The set of possible command values having the lowest cost is determined and defined as a set of selected command values. Fuel is minimized by minimizing engine power for a requested axle power. Accordingly, a fuel consumption rate requested value is determined based on an air-per-cylinder (APC) requested value.

Fuel economy optimization using air-per-cylinder (APC) in MPC-based powertrain control

A propulsion system, control system, and method are provided for optimizing fuel economy, which use model predictive control systems to generate a plurality of sets of possible command values and determine a cost for each set of possible command values based on weighting values, a plurality of predicted values, and a plurality of requested values. The set of possible command values having the lowest cost is determined. A linearized axle torque requested value and a linearized axle torque measured value are each created by subtracting an estimated disturbance. The estimated disturbance is determined based on a model of a relationship between measured engine output torque and measured transmission ratio. The linearized axle torque measured value is used to compute the predicted values, which are used to determine the cost. The linearized axle torque requested value is also used to determine the cost.

Linearized model based powertrain MPC

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.

Method for controlling an injector for injecting a reductant into an exhaust system of an internal combustion engine

A propulsion system, control system, and method are provided for optimizing fuel economy, which use model predictive control systems to generate a plurality of sets of possible command values and determine a cost for each set of possible command values of based on a first predetermined weighting value, a second predetermined weighting value, a plurality of predicted values, and a plurality of requested values. The set of possible command values having the lowest cost is determined and defined as a set of selected command values. Arbitration is performed including at least one of the following: A) determining at least one requested value based on arbitrating between a driver requested value and an intervention requested value; and B) determining a desired command value by arbitrating between a selected command value of the set of selected command values and a command intervention value.

Coordination of torque interventions in MPC-based powertrain control

A propulsion system, control system, and method are provided for optimizing fuel economy, which use model predictive control systems to generate first and second predicted actual axle torques and first and second predicted actual fuel consumption rates based on first and second sets of possible command values, respectively. The sets of possible command values include commanded engine output torques and commanded transmission ratios. First and second costs are determined for the first and second sets of possible command values, respectively, based on a first predetermined weighting value, a second predetermined weighting value, the first and second predicted actual axle torques, respectively, the first and second predicted actual fuel consumption rates, respectively, an axle torque requested, an engine output torque requested, a transmission ratio requested, and a fuel consumption rate requested. One of the first and second sets of possible command values is selected and set based on the lower cost.

Propulsion system control with MPC

A powertrain control system for a motor vehicle having a transmission and an engine includes an axle torque controller that determines a desired engine torque and a desired speed ratio from a plurality of inputs, an engine controller that determines a commanded engine torque based on the desired engine torque, wherein the commanded engine torque is used to control the engine to produce an actual engine torque, a transmission controller that determines a commanded gear ratio based on the desired gear ratio, wherein the commanded gear ratio is used to control the transmission to produce an actual gear ratio, and an estimator that determines an actual axle torque of the motor vehicle from the actual engine torque and the actual gear ratio. The plurality of inputs includes a desired axle torque, the actual axle torque, a desired fuel rate, an actual fuel rate.

Axle torque control system for a motor vehicle

A torque requesting module generates a first torque request for a spark ignition engine based on driver input. A torque conversion module converts the first torque request into a second torque request. A model predictive control (MPC) module determines a set of target values based on the second torque request, a model of the engine, and a matrix having dimensions of (m+n) by (m+n). n is an integer greater than zero that is equal to a number of lower boundary constraints used in the determination of the set of target values. m is an integer greater than zero that is equal to a number of constraints used in the determination of the set of target values other than the lower boundary constraints. An actuator module controls opening of an engine actuator based on a first one of the target values.

Model predictive control systems and methods for internal combustion engines

A prediction module generates predicted engine operating parameters for a set of possible target values based on a plurality of values indicative of states of the engine and a first set of predetermined values set based on characteristics of the engine. A parameter estimation module determines one or more estimated operating parameters of the vehicle based on the plurality of values indicative of states of the engine and a second set of predetermined values. A cost module determines a cost for the set of possible target values based on the predicted engine operating parameters. A selection module, based on the cost, selects the set of possible target values from a group including the set of possible target values and N other sets of possible target values, wherein N is an integer greater than zero, and sets target values based on the selected set of possible target values.

Estimation systems and methods with model predictive control

A system according to the present disclosure includes a model predictive control (MPC) module, an actuator module, and a remedial action module. The MPC module performs MPC tasks that include predicting operating parameters for a set of possible target values and determining a cost for the set of possible target values based on the predicted operating parameters. The MPC tasks also include selecting the set of possible target values from multiple sets of possible target values based on the cost and setting target values to the possible target values of the selected set. The actuator module controls an actuator of an engine based on at least one of the target values. The remedial action module selectively takes a remedial action based on at least one of an amount of time that elapses as the MPC tasks are performed and a number of iterations of the MPC tasks that are performed.

System and method for managing the period of a control loop for controlling an engine using model predictive control

A system according to the principles of the present disclosure includes a desired capacity module, an anticipated torque request module, and an engine actuator module. The desired capacity module generates a desired torque capacity of an engine at a future time based on a present torque request and a maximum torque output of the engine. The anticipated torque request module generates an anticipated torque request based on the desired torque capacity. The engine actuator module controls an actuator of the engine at a present time based on the anticipated torque request.

System and method for adjusting a torque capacity of an engine using model predictive control

A control system includes a control module that receives a first request corresponding to a control value for at least one of a plurality of actuators, selectively receives a second request associated with a predicted future control value for at least one of the plurality of actuators, determines a target value for the actuator based on the first request if the second request was not received, and generates a reference signal representing the second request if the second request was received. The reference signal indicates at least one of a predicted increase in the control value and a predicted decrease in the control value. A model predictive control module receives the reference signal and adjusts one of the plurality of actuators associated with the predicted future control value based on the reference signal.

Artificial output reference for model predictive control

An engine control method includes: generating a first predicted engine output torque and a first predicted mass of air per cylinder (APC) based on a model of the spark ignition engine and a first set of possible target values determined based on an engine torque request; generating a second predicted engine output torque and a second predicted mass of APC based on the model of the spark ignition engine and a second set of possible target values determined based on the engine torque request; determining a first cost for the first set of possible target values; determining a second cost for the second set of possible target values; selecting one of the first and second sets based on the first and second costs; and setting target values based on the possible target values of the selected one of the first and second sets.

A system according to the principles of the present disclosure includes a model predictive control (MPC) module and an actuator module. The MPC module generates predicted parameters based on a model of a subsystem and a set of possible target values. The MPC module generates a cost for the set of possible target values based on the predicted parameters and at least one of weighting values and references values. The MPC module adjusts the at least one of the weighting values and the reference values based on a desired rate of change in an operating condition of the subsystem. The MPC module selects the set of possible target values from multiple sets of possible target values based on the cost. The actuator module adjusts an actuator of the subsystem based on at least one of the target values.

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Siena, Italy

Daniele Bernardini