Gm Global Technolgoy Operations, Inc.

The University of Waterloo

Reza Hajiloo

SeyedAlireza Kasaiezadeh Mahabadi

Seyedeh Asal Nahidi

Ehsan Asadi

Bakhtiar Brian Litkouhi

Shamim Mashrouteh

Seyedalireza Kasaiezadeh Mahabadi

Gianmarc Coppola

Naser Mehrabi

Yubiao Zhang

Hualin Tan

Amir Khajepour

James Heath Holbrook

Jin-Jae Chen

Mansour Ataei

Ruixing Long

Arash Hashemi

Mustafa Hakan Turhan

Mohammad Pournazeri

Ali Reza Armiyoon

Ehsan Mohammadbagher

A vehicle wheel force control system includes multiple vehicle wheel height sensors, a vehicle IMU configured to measure lateral and longitudinal acceleration, and a vehicle control module is configured to obtain vehicle wheel height parameters from the multiple vehicle wheel height sensors, obtain a vehicle lateral and longitudinal acceleration parameters from the vehicle IMU, estimate a pitch arm rotation value of the vehicle, determine a pitch center of the vehicle based on the pitch arm rotation value, the vehicle wheel height parameters and the vehicle lateral acceleration parameter and the vehicle longitudinal acceleration parameter, calculate a center of gravity of the vehicle according to the determined pitch center, determine an estimated normal force on at least one wheel of the vehicle based, at least in part, on the calculated center of gravity of the vehicle, and control operation of at least one vehicle component according to the estimated normal force.

Vehicle wheel force control systems

A method for downforce control includes receiving sensor data from sensors, using a feedforward control to determine a first requested normal force at the first axle of the vehicle and a second requested normal force at the second axle of the vehicle and the sensor data, using a feedback control to determine a first requested normal force adjustment at the first axle of the vehicle and a second requested normal force adjustment at the second axle of the vehicle using the sensor data, fusing the first requested normal force at the first axle of the vehicle with the first requested normal force adjustment to determine a first-adjusted normal force request at the first axle, and fusing the second requested normal force with the second requested normal force adjustment to determine a second-adjusted normal force request at the second axle.

Method and system for determining the desired tire grip in active downforce control

A system for vehicle motion control (VMC) performance enhancement utilizing real-time data reliability and criticality assessments includes a vehicle having sensors and actuators. The sensors collect real-time dynamic state information about the vehicle. The actuators actively and continuously adjust the dynamic state of the vehicle. The system executes a VMC application that obtains, from the sensors and actuators, dynamic state information of the vehicle, and estimates a real-time vehicle dynamic state from the dynamic state information. The VMC application determines a signal criticality and a signal reliability for vehicle dynamic state information, and executes a VMC strategy based on the real-time vehicle dynamic state, the signal criticality and reliability. The VMC application detects and mitigates signal degradation by enacting a VMC strategy, and generates a VMC output command to the actuators based on the VMC strategy. The VMC strategy actively, continuously, and automatically adapts to seamlessly overcome signal degradation.

Vehicle motion control performance enhancement using real-time data reliability and criticality assessments

A method for controlling an autonomous vehicle includes receiving road data. The road data includes information about a plurality of potential events along the road ahead of the autonomous vehicle. The method further includes determining, in real time, a probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur while the autonomous vehicle moves along the road and determining, in real time, an adjusted planned path using a probabilistic predictive control that takes into account the probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur. Further, the method includes controlling the autonomous vehicle to cause the autonomous vehicle to autonomously follow the adjusted planned path.

Architecture and methodology for defensive autonomous driving using a probabilistic control strategy

A system for managing chassis and driveline actuators of a motor vehicle includes a control module executing program code portions that: cause sensors to obtain vehicle state information, receive a driver input and generate a desired dynamic output based on the driver input and the vehicle state information, and then estimate actuator actions based on the vehicle state information, generate one or more control action constraints based on the vehicle state information and estimated actuator actions, generate a reference control action based on the vehicle state information, the estimated actions of the one or more actuators and the control action constraints, and integrate the vehicle state information, the estimated actuator actions, desired dynamic output, reference control action and the control action constraints to generate an optimal control action that falls within a range of predefined actuator capacities and ensures driver control of the vehicle.

Architecture and model predictive control-based methodology to manage chassis and driveline actuators

A method for vehicle motion control includes receiving sensor data from a plurality of sensors of a vehicle and monitoring a vehicle response of the vehicle using the sensor data. The vehicle response is represented by a plurality of vehicle-response signals. The method further includes fusing the plurality of vehicle-response signals to obtain at least one fused signal. The method further includes determining whether to activate a vehicle stability control of the vehicle based on the at least one fused signal and commanding the vehicle to activate the vehicle stability control in response to determining to activate the vehicle stability control of the vehicle based on the at least one fused signal.

Data fusion-centric method and system for vehicle motion control

A system for managing vehicle body and wheel motion control with a dual clutch differential includes sensors and actuators disposed on the vehicle, the sensors measuring real-time static and dynamic data and the actuators altering static and dynamic behavior of the motor vehicle. A control module executes program code portions stored in memory. The program code portions receive the real-time static and dynamic data; selectively prioritize torque output from a prime mover of the vehicle through the differential to driven wheels of the vehicle to control a body and the driven wheels; model and estimate clutch torque for each clutch of the dual clutch differential; model and estimate a joint clutch torque, a tire force, and corner torque; and generate a torque output for each clutch of the dual clutch differential that is selected to maintain one or more of body control, wheel control, and stability of the motor vehicle.

Management of body and wheel motion controls with dual clutch differential

A system includes a primary control module, a stability status module, and a supervisory control module. The primary control module is configured to determine at least one control action for at least one of an electronic limited slip differential and an aerodynamic actuator of a vehicle based on a driver command. The stability status module is configured to determine whether at least one component of the vehicle is stable or unstable based on an input from a sensor on the vehicle. The at least one component includes at least one of a vehicle body, a front axle, a rear axle, front wheels, and rear wheels. The supervisory control module is configured to adjust the at least one control action when the at least one component is unstable.

System and method for controlling electronic limited slip differential and active aerodynamic actuator on vehicle

A vehicle motion control system includes one or more input devices for generating one or more input signals associated with data indicative of a motion of the vehicle. The system further includes a computer, which has one or more processors. The computer further includes a non-transitory computer readable storage medium for storing instructions, such that the processor is programmed to compare a current tire state and a current tire prediction model to the data indicative of the motion of the vehicle. The processor is further programmed to calculate in real-time an adjusted tire state and an adjusted tire prediction model. The processor is further programmed to generate in real-time one or more actuation signals based on the adjusted tire state and the adjusted tire prediction model. The actuators in real-time adjust the motion of the vehicle in response to the actuator receiving the actuation signal from the processor.

Vehicle motion control system using real-time tire state correction

A system for supervisory control for eAWD and eLSD in a motor vehicle includes a control module, and sensors and actuators disposed on the motor vehicle. The sensors measure real-time motor vehicle data, and the actuators alter behavior of the motor vehicle. The control module receives the real-time data; receives one or more driver inputs to the motor vehicle; determines a status of a body of the motor vehicle; determines a status of axles of the motor vehicle; determines a status of each wheel of the motor vehicle; and generates a control signal to the actuators from the driver inputs and the body, axle, and wheel statuses. The control module also exercises supervisory control by actively adjusting constraints on the control signal to each of the actuators where actively adjusting constraints on the control signal alters boundaries of control actions in response to the one or more driver inputs.

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Waterloo, Canada

Reza Hajiloo