Gm Global Technolgoy Operations, Inc.

Other

Bharath Pattipati

Michael Livshiz

Alberto Bemporad

Daniele Bernardini

Christopher E Whitney

Hualin Tan

SeyedAlireza Kasaiezadeh Mahabadi

Paul G Otanez

Bo Yu

James L Worthing

Christopher Jay Weingartz

Seyedeh Asal Nahidi

Ruixing Long

Hassan Askari

Mateusz M Nowak

Shamim Mashrouteh

Cole Reinhold

Lapo Frascati

Michael T Sarzynski

A driver command interpreter system for a vehicle includes one or more controllers that execute instructions to receive a plurality of dynamic variables, vehicle configuration information, and driving environment conditions, and determine a target vehicle state during transient driving conditions based on the plurality of dynamic variables from the one or more sensors, the vehicle configuration information, and the driving environment conditions. The one or more controllers build a transient vehicle dynamic model based on the target vehicle state during transient driving conditions, the plurality of dynamic variables, the vehicle configuration information, and the driving environment conditions, and solve for desired zeros corresponding to the target vehicle state during transient conditions.

Driver command interpreter system determining achievable target vehicle state during steady-state and transient driving conditions

A system for adaptive tire force prediction in a motor vehicle includes a control module that executes program code portions that receive real-time static and dynamic data from motor vehicle sensors, that model forces at each tire of the motor vehicle at one or more incremental time steps, that estimate actual forces at each tire of the motor vehicle at each of the one or more incremental time steps, that adaptively predict tire forces at each tire of the motor vehicle at each of the one or more incremental time steps, that generate one or more control commands for actuators of the motor vehicle, that capture discrepancies between real-time force estimations and nominal force calculations at each tire of the motor vehicle, and that apply compensation parameters to reduce tracking errors in the one or more control commands to the one or more actuators of the motor vehicle.

Architecture and methodology of adaptive tire force prediction for integrated vehicle motion control

A propulsion system, control system, and method 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. The set of possible command values that has the lowest cost is determined and defined as a selected set of command values. In some circumstances, the MPC-determined command value may be replaced by another transmission ratio command based on override inputs. Minimum and maximum transmission ratios are determined based on the override inputs, and a constrained (or arbitrated) transmission ratio is determined therefrom. The constrained or arbitrated transmission ratio is used to determine whether to apply an MPC-determined transmission ratio or a transmission ratio based on the arbitrated transmission ratio to determine an ultimate commanded transmission ratio. Pressure(s) are commanded to a transmission pulley assembly, which is configured to implement the ultimate commanded transmission ratio.

Shift management in model predictive based propulsion system 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 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 propulsion system, control system, and method are provided that use model predictive control to generate an initial selected engine output torque value. A minimum torque limit is determined by selecting a minimum acceptable engine output torque. A maximum torque limit is determined by selecting a maximum acceptable engine output torque. A desired engine output torque value is set as: a) the minimum torque limit, if the initial selected engine output torque value is less than the minimum torque limit; b) the maximum torque limit, if the initial selected engine output torque value is greater than the maximum torque limit; or c) the initial selected engine output torque value, if the initial selected engine output torque value is neither greater than the maximum torque limit nor less than the minimum torque limit.

Torque security of 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 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

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South Lyon, MI, United States of America

Bharath Pattipati