Toyota Motor Engineering & Manufacturing North America, Inc.

University of Michigan

Mike Xuli Huang

Ilya V Kolmanovsky

Dominic M Liao-McPherson

Shinhoon Kim

Di Chen

Anna G Stefanopoulou

Kevin M Zaseck

Yushi Shibaike

Shengqi Zhang

Jason R Rodgers

Kuan X Liu

A vehicle control method in a hybrid electric vehicle including an internal combustion engine, a battery, an electric motor, and a control unit. The method includes estimating an estimated vehicle velocity trajectory, estimating an initial engine power trajectory, simulating state of charge of the battery with the vehicle velocity trajectory and the initial engine power trajectory, estimating an initial terminal co-state value, simulating backward co-state dynamics using the state of charge and vehicle velocity trajectory, to obtain a resulting co-state trajectory. The co-state trajectory is used to solve a minimization control and propagate state of charge dynamics forward in time. The method includes updating control and the co-state trajectory, adjusting the terminal co-state value, and controlling a usage of the battery and the internal combustion engine. The method can be performed to optimize the engine power trajectory to minimize fuel consumption in real time.

Stabilized real time trajectory optimization for hybrid energy management utilizing connected information technologies

The disclosure is directed to solving a full trajectory optimization problem in real-time for a hybrid electric vehicle (HEV) such that future driving conditions and energy usage may be fully considered in determining optimal engine energy usage and battery energy usage in real-time during a trip. An electronic control unit of the HEV may be configured to: receive route information for a route to be driven by the HEV; and after receiving the route information, iterating the operations of: measuring a current state of charge (SOC) of the battery; using at least the measured SOC and an initial co-state value stored in a memory, performing a process to iteratively update the co-state value to obtain an updated co-state value; using at least the updated co-state value, computing an updated control value; and applying the updated control value to control a usage of the battery and the internal combustion engine.

Real time trajectory optimization for hybrid energy management utilizing connected information technologies

A system for control of the air path of an internal combustion engine including a feed-forward controller and a feed-back controller. The feed-forward controller configured to in a sampling period, obtain model parameter values, incorporate the modeled parameter values and reference values into an optimization for a nonlinear model predictive control, perform a Newton method iteration of the optimization in order to determine a solution, and issue commands that control inputs for engine operation based on the solution. The feed-back controller configured to obtain modeled parameter values, obtain measured parameter values based on the operating condition of the engine, incorporate the modeled parameter values, measured parameter values, and reference values into an optimization for a nonlinear model predictive control, perform a Newton method iteration of the optimization in order to determine a solution, and issue commands that control inputs for engine operation based on the solution.

Feedforward and feedback architecture for air path model predictive control of an internal combustion engine

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

A system including a controller configured to, in each sampling period, minimize a distance of the autonomous vehicle from a target path by solving a constrained control problem, input sensor values and estimators that are calculated based on the sensor values and dynamic models and record the sensor values and the estimators in a memory of the controller, incorporate the sensor values and the estimators into conditions for minimizing the distance of the autonomous vehicle from the target path associated with the constrained control problem, map the conditions for minimizing the distance of the autonomous vehicle from the target path to a non-smooth system using Fischer-Burmeister function, smooth the non-smooth system and apply Newton method iterations to the smoothed system in order to converge on a solution, and issue commands including a steering command that control actuators of the autonomous vehicle based on the solution.

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

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 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.

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Ann Arbor, MI, United States of America

Mike Xuli Huang