Ford Global Technolgoies, LLC

University of Michigan

Toyota Motor Engineering & Manufacturing North America, Inc.

Massachusetts Institute of Technology

Other

Ford Motor Company Limited

Frod Global Tech., Inc.

Ilya V Kolmanovsky

Jing Sun

John Ottavio Michelini

Alex Gibson

Michiel Jacques Van Nieuwstadt

Julia Helen Buckland

Donald James Lewis

Gang Song

Mike Xuli Huang

Jeffrey A Cook

Mrdjan J Jankovic

Alexander O'Connor Gibson

Davorin David Hrovat

Steven Joseph Szwabowski

Nate Trask

Paul Eduard Moraal

Dimitar Petrov Filev

Davor Hrovat

Yan Wang

John Robert Grabowski

Mohammad Haghgooie

Vince Winstead

Alexander Anatoljevich Stotsky

Michael W Degner

Ming Lang Kuang

Alex O'Connor Gibson

Michael John Cullen

James Ervin

Thomas Megli

Anthony Mark Phillips

Anna G Stefanopoulou

David George Farmer

Xiaoying Zhang

Mazen Hammoud

Michael Rinehart

Larry Allen Hardy

Munther Dahleh

Dominic M Liao-McPherson

James William McCallum

Maria Druzhinina

Georgia-Evangelia Katsargyri

Mohammad Reza Amini

Gopichandra Surnilla

Joseph Norman Ulrey

Jianbo Lu

John David Russell

Fazal Urrahman Syed

Kwaku O Prakah-Asante

Adam Nathan Banker

Perry Robinson Macneille

Stefano Di Cairano

Marvin Paul Kraska

Fling Finn Tseng

Oleg Yurievitch Gusikhin

Dennis C Reed

Diana Yanakiev

Mathew Alan Boesch

Tobias John Pallett

Christopher John Teslak

William Goodwin

Brian Moorhead

Uros Kalabic

Vladimir V Kokotovic

Matthew John Gerhart

Jessy W Grizzle

Richard Walter Anderson

Yong-Wha Kim

Woong-chul Yang

Gopichandra Sumilla

Miroslava Jankovic

Rodney John Tabaczynski

Theodore Joseph Filippi

Suzanne Kay Wait

Mahmoud A Abou-Nasr

Hao Wang

Shinhoon Kim

Greg Blaisdell Stevens

Yiheng Feng

Lev Lezhnev

Kuan X Liu

Zhen Yang

Jason R Rodgers

Kevin McDonough

Joseph Scillieri

Mike Huang

An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.

Multi-range vehicle speed prediction using vehicle connectivity for enhanced energy efficiency of vehicles

An engine and cabin thermal management system for use with a vehicle having an engine, a cabin heating system configured to thermally heat a cabin of the vehicle, a coolant system operably coupled to the engine and to the cabin heating system to thermally manage a temperature of the engine and a temperature of the cabin. The coolant system having one or more coolant thermal storage units fluidly coupled with a radiator and heater core of the coolant system forming a coolant loop. The system further having a control system configured to monitor and maintain at least a predetermined coolant temperature at the cabin heating system even during a coolant temperature decrease at the engine stops.

Actively controlled coolant tank to increase thermal storage capacity of hybrid electric vehicles

Some implementations of the disclosure are directed to reducing or removing time lag in vehicle velocity prediction by training a model for vehicle velocity prediction using labeled features that provide indication of a feature associated with a vehicle acceleration or deacceleration event. In one implementation, a method includes: receiving multiple time series datasets, each of the time series datasets including sensor data, GPS data, and vehicle state data collected over time; extracting features from each of the time series datasets that are indicative of a future velocity of a vehicle; labeling the extracted features of each of the time series datasets to indicate vehicle acceleration or deacceleration events; and after labeling the extracted features of each of the time series datasets, using at least a subset of the extracted and labeled time series datasets to train a machine learning model that predicts vehicle velocity some time into the future.

Vehicle velocity predictor using neural networks based on V2X data augmentation to enable predictive optimal control of connected and automated vehicles

System for controlling an internal combustion engine having a variable geometry turbocharger, an EGR valve and an EGR throttle. The system includes a controller and an engine. In each sampling period, the controller inputs sensor values and estimators that are calculated based on the sensor values and dynamic models and records the sensor values and the estimators in a memory of the controller. The controller incorporates the sensor values and the estimators into conditions for optimality associated with a constrained optimal control problem, maps the conditions for optimality to a non-smooth system using Fischer-Burmeister function, performs Newton method iterations on a smoothed system approximating the non-smooth system in order to converge on a solution, and issues commands that control the EGR valve, the EGR throttle and the variable geometry turbocharger during engine operation.

Smoothed and regularized Fischer-Burmeister solver for embedded real-time constrained optimal control problems in automotive systems

Systems and methods for controlling an engine airpath include receiving, at a supervisory controller, an engine speed corresponding to a present engine speed, a fuel target corresponding to a request for torque from a driver and one or more state estimates generated by an estimator. The supervisory controller predicts, over a prediction horizon, a constraint violation in response to the engine speed, the fuel target, and the one or more state estimates using a prediction model, adjusts an EGR rate target to a modified value, when the constraint violation is predicted, and maintains the EGR rate target at a nominal value when the constraint violation is not predicted. A nonlinear predictive controller generates one or more actuator commands based on the EGR rate target, where the one or more actuator commands control an engine actuator such that an EGR rate of the engine airpath tracks the EGR rate target.

Supervisory model predictive controller for diesel engine emissions control

Methods and systems for use of model predictive control (MPC) controllers utilizing hybrid, quadratic solvers to solve a linear feasibility problem corresponding to a nonlinear problem for an internal combustion engine plant such as a diesel engine air path. The MPC solves a convex, quadratic cost function having optimization variables and constraints and directs the plant per the output solutions to optimize plant operation while adhering to regulations and constraints. The problem includes a combination of iterative and direct calculations in the primal space depending on whether a partial step (iterative) or a full step (direct) is attempted. Further, primal and dual space array matrices are pre-computed and stored offline and are retrieved via use of a unique identifier associated with a specific active set for a set of constraints. Such hybrid and/or offline calculations allow for a reduction in computational power while still maintaining accuracy of solution results.

Hybrid partial and full step quadratic solver for model predictive control of diesel engine air path flow and methods of use

A discrete time rate-based model predictive controller for air path control for a diesel engine regulates VGT position and EGR valve position to specified set points by coordinated control of intake manifold air pressure and EGR rate. The controller may be configured to measure or estimate at least one of the intake manifold pressure and EGR rate. A non-linear discrete time rate-based predictive model may be used, as developed by the controller.

Discrete time rate-based model predictive control method for internal combustion engine air path control

A method for controlling an internal combustion engine using a controller that controls an air flow path by adjusting at least one of a variable geometry turbine (VGT) and an exhaust gas recirculation (EGR) flow rate during engine operation. The method determines inputs, such as engine speed and fuel rate from the sensor data, and employs a switch based gain-scheduled explicit model predictive controller (MPC) responsive to the inputs to determine the air flow path.

Switch gain scheduled explicit model predictive control of diesel engines

A system and method for controlling an internal combustion engine include anticipating vehicle launch in response to vehicle position relative to a traffic stream and controlling automatic restart and shut down in response to an anticipated vehicle launch to prevent an automatic engine shut down or to initiate an automatic engine restart. Embodiments include determining vehicle position and traffic position using GPS coordinates. Automatic restart may be initiated in response to movement of a forward vehicle away from the vehicle or in response to a signal from a traffic control device, for example. Automatic shutdown may be inhibited in response to movement of a forward vehicle in cross traffic, in response to a turn indicator being active, or in response to wheel angle, for example.

Assisted direct start engine control for enhanced launch performance

A rate based model predictive controller for air path control for a diesel engine regulates turbine lift and EGR valve flow rate to specified set points by coordinated control of intake manifold air pressure and EGR valve flow rate. The controller utilizes a rate-based reduced order linear model for model predictive control.

Rate-based model predictive control method for internal combustion engine air path control

A rate based model predictive controller and method for air path control for a diesel engine regulates intake manifold pressure (MAP) and EGR valve flow rate to specified set points by coordinated control of a variable geometry turbine (VGT) and EGR valve position. A decay and a flexible Lyapunov function is enforced on the rate based model predictive controller for a single step prediction and control arisen.

Rate-based contractive model predictive control method for internal combustion engine air path control

A method includes utilizing navigation data from a navigation system to segment a route to be travelled by a travelling vehicle into segments each having a road grade different than neighboring segments. The method further includes discharging a battery of the vehicle as the vehicle travels along an initial segment of the route according to a state-of-charge (SoC) set-point based on the road grade of the initial segment and a variable representative of the road grades of at least two other segments of the route.

Electric vehicle and method of battery set-point control

Methods and systems are provided for improving surge control. When surge conditions are approached, a reference governor reduces engine airflow at a slower rate and to a higher level than the engine airflow required to meet the reduced torque demand. The excess torque resulting from the extra airflow is offset by applying a negative torque on the driveshaft via an electric machine coupled to the engine or via alternate engine actuator adjustments.

Methods and systems for torque control

Periodic torque disturbances that affect cylinder valve operation are anticipated, and an appropriate reaction torque is provided by an electric motor to counteract the periodic torque disturbances to position a cylinder valve at a desired position.

Feed forward control for electric variable valve operation

Vehicle apparatus adjusts a vehicle powertrain of the vehicle in response to a speed setpoint. An optimizer selects a control policy to periodically generate speed adjustments for applying to the speed setpoint to operate at increased efficiency. The control policy is based on a value function providing an optimized solution for a cost model and a transition probability model. The transition probability model corresponds to a driving state defined according to a plurality of dimensions including a time-of-day dimension and a geographic region dimension. The transition probability model and the control policy have inputs based on road grade and speed. The optimizer collects road grade data during routine driving of the vehicle to construct a observed transition probability model and uses divergence between the observed transition probability model and a set of predetermined transition probability models to identify a control policy for use during the routine driving.

On-board real-time speed control setpoint variation using stochastic optimization

Growing community of inventors

Novi, MI, United States of America

Ilya V Kolmanovsky