Ford Global Technolgoies, LLC

Honeywell International Inc.

Massachusetts Institute of Technology

University of Michigan

John Ottavio Michelini

Donald James Lewis

Ilya V Kolmanovsky

Dimitar Petrov Filev

Steven Joseph Szwabowski

Gang Song

John David Russell

Christopher Paul Glugla

Anthony Mario D'Amato

Gopichandra Surnilla

Imad Hassan Makki

Nate Trask

Alex Gibson

Michael John Cullen

Davor Hrovat

James Michael Kerns

Fling Finn Tseng

Alexander O'Connor Gibson

Thomas Megli

Jonathan Thomas Mullen

Jianbo Lu

Diana Yanakiev

Yan Wang

John M Roth

Shankar Narayan Mohan

Michael Flory

Ming Lang Kuang

Mrdjan J Jankovic

Carol Louise Okubo

Jaroslav Pekar

Anthony Mark Phillips

Timothy Mark Feldkamp

Xiaoying Zhang

Alexander Y Gopp

Ondrej Santin

Michael Rinehart

Larry Allen Hardy

Munther Dahleh

Georgia-Evangelia Katsargyri

Aed M Dudar

Ross Dykstra Pursifull

Jeffrey Allen Doering

Alex O'Connor Gibson

David Karl Bidner

Robert Roy Jentz

Kwaku O Prakah-Asante

Stefano Di Cairano

Shane Elwart

Thomas Edward Pilutti

Mathew Alan Boesch

Stephen B Smith

Rani Kiwan

Eric Hongtei Tseng

Yonghua Li

Aaron Lewis Mills

Hsin-Hsiang Yang

Pankaj Kumar

John Shutko

Shunsuke Okubo

Joseph F Stanek

Stephen Lee Cooper

Finn Finn Tseng

Medville Jay Throop

Mahmoud A Abou-Nasr

Ryan Lee Baker

Thomas Francis Rolewicz, Jr

Jing Wang

Craig Thomas Hodorek

James William McCallum

Christopher Short

Matthew Aaron Knych

Dimitar P Feliv

Mahmoud Abou-Nasr, I

Engin Ozatay

Michio Sugeno

Luka Eciolaza

Tadanari Taniguchi

Perry R McNeille

Kevin McDonough

Methods, apparatus, systems and articles of manufacture to calibrate a weight estimation are disclosed herein. An example apparatus comprises memory including stored instructions, and a processor to execute the instructions to determine, via a first sensor, a first load estimation of a vehicle corresponding to when the vehicle under a load condition, determine, via a second sensor, a second load estimation corresponding to when the vehicle is moving and under the load condition, and calibrate, based on a difference between the second load estimation and the first load estimation, an error of the first load estimation.

Methods and apparatus to calibrate a weight estimation

Methods and systems are provided for cruise control velocity tracking. In one example, the method or system may generate a torque command output via a velocity controller that allows for an error within bounds to reduce a fuel consumption amount, the torque command output selected from outcomes of a leader and follower game.

Methods and systems for cruise control velocity tracking

Methods and systems are provided for controlling vehicle torque output during cruise control. In one example, a method may include determining future vehicle torque output by minimizing an objective function based on an instantaneous vehicle speed, an average vehicle speed, and a present vehicle torque output. The weights of the objective function may be updated based on the past and the present vehicle operating parameters.

Methods and systems for cruise control

An object exterior to a vehicle can be detected, and an augmented image of the detected object can be output to a vehicle display device, wherein at least one of dimensions and a motion of the augmented image is adjusted based on an environmental condition and physical attribute of the detected object including at least one of a speed, an acceleration, a location, and a direction of the detected object.

Vehicle display with augmented realty

Methods and systems for adjusting vehicle operation in response to vehicle weight are described. In one example, an adaptive driver demand correction is adjusted in response to vehicle weight. The methods and systems may provide for more consistent powertrain response and lower vehicle emissions at lower vehicle weights.

System and method for improving vehicle performance

Data about vehicle movement at a stoplight are collected. A stoplight cycle time is predicted with a probability model. The data are compared to the predicted stoplight cycle time. A noise function is applied to the data to generate noise-applied data. The probability model for the predicted stoplight cycle time is updated by scaling the probability model with the noise-applied data to generate a new probability model. A recommended vehicle operation is provided via a network to at least one vehicle computer based on the predicted stoplight cycle time determined by the new probability model.

Rapid traffic parameter estimation

A controller includes a processor programmed to determine, for a vehicle, a first control input based on input data and first reference parameters. The processor is further programmed to operate the vehicle according to the first control input. Based on operating data of the vehicle for an operating condition, the processor determines a second control input for the vehicle. Operating the vehicle according to the second control input reduces a cost of operating the vehicle relative to operating the vehicle according to the first control input. The processor is further programmed to determine, based on the second control input, second reference parameters. The controller generates a third control input based on the second reference parameters and the input data. A cost of operating the vehicle according to the third control input is reduced relative to the cost of operating the vehicle based on the first control input.

Adaptive vehicle control

A method for operating an actuator is disclosed. The actuator may be a linear or non-linear actuator. In one example, a pseudo-inverse piecewise bilinear model is adapted in response to output of a feedback controller to improve feed forward control.

System and method for feedback error learning in non-linear systems

Methods and systems are presented for improving performance of a vehicle operating in a cruise control mode where a controller adjusts torque output from a vehicle to maintain vehicle speed within a desired range while preventing the unnecessary downshifts. The methods and systems include adapting a vehicle dynamics model and a vehicle fuel consumption model that provide input to nonlinear model predictive controller.

Method and system for controlling shifting of a vehicle in cruise control

A vehicle system includes a processor having a memory. The processor is programmed to predict a vehicle key-on time based on a next destination of a vehicle. The processor is further programmed to request, at a predetermined amount of time before the key-on time, optimization data from a remote server.

Predictive vehicle task scheduling

Method and system are provided for vehicle route planning based on adaptive model predictive control. In one example, a method may include real-time online identification of the vehicle model base on the vehicle inputs and outputs; compression of the input space to increase the optimization efficiency; and optimization of the route planning based on the model parameter of the vehicle and the known road grade.

Adaptive model predictive control for vehicle route planning

Traffic incident information may be clustered to determine geographical locations in which a statistically-increased level of traffic incidents occur. Indices indicative of vehicle operating conditions within the locations may be generated according to crowd-sourced event information factors and an index function including coefficients defining contributions of the factors to the indices. The coefficients may be optimized to minimize error between the index-predicted statistically-increased level areas and the clustered traffic incident information.

Personalized route indices via crowd-sourced data

Methods and systems are presented for improving performance of a vehicle operating in a cruise control mode where a controller adjusts torque output from a vehicle to maintain vehicle speed within a desired range. The methods and systems include adapting a vehicle dynamics model and a vehicle fuel consumption model that provide input to nonlinear model predictive controller.

Method and system for vehicle cruise control

Methods and systems are provided for improving the frequency of attempting and successfully completing one or more on-board diagnostic routines. Engine operating conditions are predicted based on a vehicle operator's driving pattern and routines are initiated if the predicted conditions match the conditions required for performing the routine. If the conditions do not match, entry and/or execution conditions of the routine are adjusted to better match the predicted conditions, so as to enable the routine to be attempted.

Method for triggering a vehicle system monitor

A method according to an exemplary aspect of the present disclosure includes, among other things, scheduling charging of an energy storage device of an electrified vehicle based on a learned key-on pattern. The learned key-on pattern is derived by recursively updating the probability that a subsequent key-on event is likely to occur at any given time and day.

Pattern based charge scheduling

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Livonia, MI, United States of America

John Ottavio Michelini