Gm Global Technolgoy Operations, Inc.

Naser Mehrabi

SeyedAlireza Kasaiezadeh Mahabadi

Hualin Tan

Seyedeh Asal Nahidi

Saurabh Kapoor

Bakhtiar Brian Litkouhi

Ehsan Asadi

James Heath Holbrook

Seyedalireza Kasaiezadeh Mahabadi

Yubiao Zhang

Sresht Gurumoorthi Annadevara

Hassan Askari

Reza Hajiloo

Arash Hashemi

Mustafa Hakan Turhan

Mohammad Pournazeri

Ruixing Long

Jin-Jae Chen

Shamim Mashrouteh

Mansour Ataei

Gianmarc Coppola

Lapo Frascati

Amir Abolhassani

Ali Reza Armiyoon

Nauman Sohani

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 method for downforce control includes receiving a plurality of vehicle inputs from a vehicle. The plurality of vehicle inputs includes sensor data from a plurality of sensors of the vehicle. The method further includes determining a downforce acting on the vehicle, by: (a) determining a predicted half-car model uncertainties using a neural network; and (b) determining a front normal force at the front axle and a rear normal force at the rear axle using the vehicle inputs, the predicted half-car model uncertainties, and a half-car model. The method further includes determining a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the front normal force at the front axle and a rear normal force at the rear axle, respectively.

Method and system for downforce estimation using a half-car model with a data driven uncertainties estimator

A method for estimating a lateral velocity of a vehicle includes receiving sensor data from a sensor of the vehicle, determining a physics-based longitudinal velocity estimation of the vehicle using a physics-based model and the sensor data, determining a data-driven longitudinal velocity estimation of the vehicle using a first neural network and the sensor data, determining, using a second neural network, which of the physics-based longitudinal velocity estimation and the data-driven longitudinal velocity estimation is more reliable to determine a selected longitudinal velocity estimation, determining the lateral velocity of the vehicle using the selected longitudinal velocity estimation, and controlling the vehicle based on the lateral velocity.

Real-time reliability assessment method to enhance robustness of data-fusion based vehicle speed estimation

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 fault remediation system for a vehicle includes one or more controllers in electronic communication with one or more consumed interfaces and one or more provided interfaces. The one or more controllers execute instructions to receive, from the one or more consumed interfaces, a consumed signal and perform fault detection upon the consumed signal to determine the presence of an active fault within the consumed signal. In response to detecting an active fault with the consumed signal, the one or more controllers select a remediation state from a group of two or more prospective remediation states based on a significance analysis of the consumed signal. The one or more controllers evaluate a relevant subfunction that corresponds to the consumed signal that the remediation state addresses for the presence of remediation tolerance and generates arbitration instructions based on the remediation tolerance.

Fault remediation system for a vehicle

Systems and methods for determining whether a vehicle is in an understeer or oversteer situation. The system includes a controller circuit coupled to an IMU and an EPS, and programmed to: calculate, for a steered first axle, an axle-based pneumatic trail for using IMU measurements and EPS signals and estimate a saturation level as a function of a distance between the axle-based pneumatic trail and zero. The system estimates, for an unsteered second axle, an axle lateral force curve with respect to a slip angle of the second axle, and a saturation level as a function of when the axle lateral force curve with respect to the slip angle transitions from positive values to negative values. The saturation level of the first axle and the second axle are integrated. The system determines that the vehicle is in an understeer or oversteer situation as a function of the integrated saturation levels.

Systems and methods for determining whether a vehicle is in an understeer or oversteer situation

A method for determining, in real-time, an electronic limited-slip differential (eLSD) clutch torque includes receiving vehicle data in real-time, wherein the vehicle data includes a torque request, determining a preliminary eLSD clutch torque using a neural network and the vehicle data, determining clutch torque bounds of the eLSD using a physics-based model, determining whether the preliminary eLSD clutch torque is outside the clutch torque bounds of the eLSD, adjusting the preliminary eLSD clutch torque using clutch torque bounds to determine a final clutch torque of the eLSD in response to determining that the preliminary eLSD clutch torque is outside the clutch torque bounds of the eLSD, and commanding, in real-time, the eLSD to apply the final clutch torque to a clutch of the eLSD.

Real-time machine learning and physics-based hybrid approach to perform eLSD torque estimation

An aerodynamic deflector on the vehicle is repositionable. An actuator is coupled with the aerodynamic deflector. A controller configured to: detect a performance mode of operation of the vehicle; determine a requested lateral acceleration; calculate a control adjustment of the aerodynamic deflector to generate a downforce to achieve the requested lateral acceleration and maximize lateral grip of the vehicle; and operate the actuator to effect the control adjustment of the aerodynamic deflector to generate the downforce on the vehicle.

System and method for optimal vehicle downforce allocation

Systems and methods for controlling a vehicle are provided. The systems and methods include a sensor system and a processor configured to execute program instructions, to cause the at least one processor to: receive yaw rate values, lateral acceleration values and longitudinal velocity values for the vehicle from the sensor system, determine side slip angle parameter values based on the yaw rate values, lateral acceleration values and longitudinal velocity values, determine phase portrait angles based on the side slip angle parameter values and the yaw rate values, wherein the phase portrait angles each represent an angle between yaw rate and side slip angle for the vehicle in a phase portrait of yaw rate and side slip angle, detect or predict vehicle instability based at least on the phase portrait angles, and when vehicle instability is detected or predicted, control motion of the vehicle to at least partly correct the vehicle instability.

Systems and methods for predicting and detecting vehicle instability

A system includes a clutch state module and a clutch torque module. The clutch state module is configured to determine whether a clutch of an electronic limited slip differential is locked or unlocked. The electronic limited slip differential couples an engine of a vehicle to left and right wheels of the vehicle. The clutch torque module is configured to estimate an actual torque transferred by the clutch using a first clutch torque model when the electronic limited slip differential is unlocked, and estimate the actual clutch torque using a second clutch torque model when the electronic limited slip differential is locked. The second clutch torque model is different than the first clutch torque model.

System and method for estimating a clutch torque of an electronic limited slip differential and tire longitudinal forces

A vehicle, system and method of operating the vehicle. A sensor measures a dynamic parameter of the vehicle. A processor determines a lateral force on a first tire based on the dynamic parameter of the vehicle, determines a longitudinal force on the first tire that achieves a maximal grip of the first tire for the lateral force, and adjusts a first torque on the first tire in order to achieve the determined longitudinal force at the first tire.

Architecture and methodology for real-time target wheel slip identification to optimally manage wheel stability and vehicle lateral grip

A traction control module includes a sensor/estimation module configured to output wheel stability data based on a plurality of wheel condition inputs and a wheel stability monitoring module configured to calculate a plurality of wheel stability predictors based on the wheel stability data. Each of the wheel stability predictors is independently indicative of a wheel slip condition. The traction control module further includes a wheel stability data fusion module configured to receive each of the plurality of wheel stability predictors, combine selected wheel stability predictors from the plurality of wheel stability predictors to generate combinations of the wheel stability predictors, and selectively output a torque reduction request based on the combinations of the wheel stability predictors.

Apparatus and methodology for wheel stability monitoring system

An axle torque distribution system includes a memory and a control module. The memory stores a steering angle and a toque distribution algorithm. The control module executes the torque distribution algorithm to: obtain the steering angle; based on the steering angle, determine total lateral force requested for axles of a vehicle; based on the total lateral force requested, determine lateral forces requested for the axles while constraining lateral force distribution between the axles, where the constraining of the lateral force distribution includes, based on maximum lateral force capacities of tires of the vehicle, limiting the lateral forces requested for the axles; determine available longitudinal capacities for the axles based on the lateral forces requested respectively for the axles; determine torque capacities of the axles based on the lateral forces requested respectively for the axles; and control distribution of torque to the axles based on the torque capacities of the axles.

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Richmond Hill, Canada

Naser Mehrabi