Gm Global Technolgoy Operations, Inc.

Shamim Mashrouteh

SeyedAlireza Kasaiezadeh Mahabadi

Seyedeh Asal Nahidi

Reza Hajiloo

Ehsan Asadi

Bakhtiar Brian Litkouhi

Hassan Askari

Yubiao Zhang

Ruixing Long

Hualin Tan

James Heath Holbrook

Naser Mehrabi

Saurabh Kapoor

Bharath Pattipati

Mansour Ataei

Arash Hashemi

Amir Abolhassani

Lapo Frascati

A data health monitoring and recording system for a vehicle includes a data recording infrastructure and one or more controllers. The one or more controllers include a multi-layer control software architecture including two or more software layers. The one or more controllers execute instructions to monitor, by a health monitoring structure that is part of the multi-layer control software architecture, one or more parameters calculated by each of the two or more software layers. The one or more controllers compare a respective parameter of a respective software layer with a predetermined corresponding performance metric for a required time horizon. In response to determining the respective parameter of the respective software layer does not meet the predetermined corresponding performance metric for the required time horizon, a trigger that instructs the data recording infrastructure to record the parameters calculated by each of the two or more software layers is generated.

Vehicle data health monitoring and recording system for multi-layer control software architecture

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 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 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.

Supervisory control for e-AWD and e-LSD

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Markham, Canada

Shamim Mashrouteh