Gm Global Technolgoy Operations, Inc.

Ning Jin

Christopher E Whitney

Todd R Shupe

Ruixing Long

Robert Charles Simon, Jr

Michael Livshiz

Kevin C Wong

Birendra P Bhattarai

Gary Robert Cygan, Jr

Julian R Verdejo

Alberto Bemporad

Daniele Bernardini

John L Lahti

Vivek Mehta

Nicola Pisu

Jeffrey M Kaiser

William Leonard Aldrich, Iii

James L Worthing

Jyh-shin Chen

Andrew W Baur

Scott Joseph Chynoweth

Robert J Genslak

Klaus Pochner

Etsuko Muraji Stewart

Cheryl A Williams

Ronald W Van Diepen

Jun Lu

Enrico Tropschug

Anthony H Heap

Paul Alan Bauerle

William R Cawthorne

Shushan Bai

Richard B Jess

Layne K Wiggins

Gregory Paul Matthews

Matthew D Whitton

Rafat F Hattar

Leonard G Wozniak

Adam J Heisel

Krunal P Patel

Douglas J Babcock

Timothy John Hartrey

Vincenzo Alfieri

Christopher Harold Knieper

Mark A Schang

Michael L Kociba

R Travis Schwenke

David N Hayden

Nathan M Picot

James B Nicholson

Steven G Bryde

Weixin Yan

Brandon M Fell

Sharon L Storch

Helmut Oswald

James A Shore

Daniel N Wehrwein

Donald Chan

Martin Weber

Gregory M Wilson

A fuel management system includes a memory and a control module. The memory stores fuel rate maps for multiple firing fractions, where: each of the firing fractions corresponds to a respective firing pattern of an engine; at least some of the firing patterns include deactivating one or more cylinders. The control module: for each of the firing fractions, determines a fuel efficiency value for each of multiple transmission gear ratios, where fuel efficiency values are provided for transmission ratio and firing fraction pairs; applies drive ability constraints to provide resultant transmission ratio and firing fraction pairs; subsequent to applying the drive ability constraints and based on the fuel efficiency values, selects one of the resultant transmission ratio and firing fraction pairs; and concurrently operates a transmission and the engine according to the selected one of the transmission ratio and firing fraction pairs.

Fuel efficiency based integrated engine firing fraction and transmission ratio selection

An engine control system includes a prediction module that, during an exhaust stroke of a first cylinder of an engine, determines a predicted intake manifold pressure at an end of a next intake stroke of a second cylinder following the first cylinder in a firing order of the cylinders. An air per cylinder (APC) module determines a predicted mass of air that will be trapped within the second cylinder at the end of the next intake stroke of the second cylinder based on the predicted intake manifold pressure. A fueling module controls fueling of the second cylinder during the next intake stroke based on the predicted mass of air.

Intake manifold and cylinder airflow estimation systems and methods

A method, control system, and propulsion system use model predictive control to control and track several parameters for improved performance of the propulsion system. Numerous sets of possible command values for a set of controlled variables are determined. Initial constraints for the controlled variables are determined, which include upper and lower limits for each controlled variable and upper and lower rate-of-change limits for each controlled variable. A set of consolidated constraint limits for the controlled variables is then determined. Each consolidated constraint limit is determined by consolidating one of the upper and lower limits with one of the upper and lower rate-of-change limits. A cost for each set of possible command values is determined, and the set of possible command values that has the lowest cost and falls within the set of consolidated constraint limits is selected for use in controlling the propulsion system.

Consolidation of constraints in model predictive control

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.

Method to control engine using airflow actuators

A method for controlling an actuator system of a motor vehicle includes utilizing a model predictive control (MPC) module with an MPC solver to determine optimal positions of a plurality of actuators subject to constraints, optimizing a cost function for a set of actuator duty cycles for controlling positions of the plurality of actuators, determining if the MPC solver has determined optimal actuator positions for the plurality of actuators, and applying a linear quadratic regulator (LQR) solution if the MPC solver fails to determine optimal actuator positions for the plurality of actuators.

Model predictive control systems and methods for increasing computational efficiency

A system includes a residual per cylinder calculation module that calculates an amount of residual (i.e., trapped exhaust gas) within an engine cylinder. A temperature calculation module calculates a temperature within the engine cylinder based on the amount of residual. A first air per cylinder (APC) calculation module calculates total charge content within the engine cylinder based on the temperature and calculates a first amount of air trapped within the engine cylinder based on the total charge content and the amount of residual. A residual mass fraction (RMF) calculation module calculates an RMF of the exhaust gas based on the amount of trapped exhaust gas at EVC and the total charge content. A second APC calculation module determines a backflow of the charged content into the engine cylinder and calculates a second amount of air trapped within the engine cylinder based on the backflow and the first amount of air.

Physics based single cylinder charging model

A tangible computer readable medium of a vehicle includes object code referencing a plurality of variables, the object code for: identifying sets of possible target values based on air and exhaust setpoints for an engine; generating predicted parameters based on a model of the engine and the sets of possible target values, respectively; selecting one of the sets of possible target values based on the predicted parameters; setting target values based on the selected one of the sets of possible target values, respectively; and controlling opening of a throttle valve based on a first one of the target values. The tangible computer readable medium also includes calibration data stored separately and that includes predetermined values for the variables referenced in the object code, respectively. At least one processor executes the object code using the predetermined values to perform the identifying, the generating, the selecting, the setting, and the controlling.

Calibration systems and methods for model predictive controllers

A requesting module generates a first torque request for an engine based on driver input. A conversion module converts the first torque request into a second torque request. A model predictive control (MPC) module determines a current set of target values based on the second torque request, a model of the engine, a tableau matrix, and a basic solution matrix. The MPC module: initializes the basic solution matrix to a predetermined matrix that is dual feasible; selectively iteratively updates the basic solution matrix and columns of the tableau matrix; determines changes for the target values, respectively, based on entries of the basic solution matrix resulting from the selective iterative updating; and determines the current set of target values by summing the changes with a last set of target values, respectively. An actuator module controls an engine actuator based on a first one of the current set of target values.

An engine control system for a vehicle may include a sequence determination module that generates a first set of possible MPC target values and a second set of possible MPC target values. A cost module determines a first cost for the first set of possible MPC target values and a second cost for the second set of possible MPC target values. A selection module that selects MPC target values from one of the first and second sets of possible MPC target values based on the first and second costs. A transition module that receives the MPC target values, compares the MPC target values with a plurality of previous control requests, and selects a set of target values ranging from the previous control requests to the MPC target values that control a plurality of engine functions.

Non-model predictive control to model predictive control transitions

A model predictive control (MPC) module performs multiple iterations to determine a current set of target values for an engine based on a torque request, a model of the engine, a tableau matrix, and a basic solution matrix. Each of the iterations includes: determining whether the basic solution matrix is not in standard form; when the basic solution matrix is not in standard form, identifying a first column of the tableau matrix based on a first entry position of the primal variable of the non-basic pair; when the basic solution matrix is in standard form and one or more primal variables of the basic solution matrix are negative, identifying the first column of the tableau matrix based on a second entry position of a dual variable of the basic pair; and selectively updating the basic solution matrix based on the first column of the tableau matrix.

Model Predictive control systems and methods for increasing computational efficiency

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 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 setpoint module generates setpoints for the spark ignition engine based on the second torque request. A model predictive control (MPC) module: identifies sets of possible target values based on the setpoints; generates predicted parameters based on a model of the spark ignition engine and the sets of possible target values, respectively; selects one of the sets of possible target values based on the predicted parameters; and sets target values based on the possible target values of the selected one of the sets. A first constraint module selectively sets a predetermined range for first one of the target values. The MPC module limits the first one of the target values to within the predetermined range.

Airflow control systems and methods using 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.

System and method for improving the response time of an engine using model predictive control

For an upshift of a transmission, a model predictive control (MPC) module sets target intake and exhaust valve timings for changes in a torque request that occur during the upshift. A phaser actuator module controls intake valve phasing of an engine based on the target intake valve timing and controls exhaust valve phasing based on the target exhaust valve timing.

Engine control systems and methods for transmission upshifts

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Novi, MI, United States of America

Ning Jin