Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Jidosha Kabushiki Kaisha

University of Michigan

Shinhoon Kim

Mike Xuli Huang

Ilya V Kolmanovsky

Dominic M Liao-McPherson

Systems and methods described herein concern estimating the mass of a vehicle. One embodiment uses a slope estimator to estimate the grade of a roadway on which the vehicle is traveling; inputs state estimates from the slope estimator, an estimated wheel force, and an estimated wheel acceleration to a secondary mass estimator that calculates a first estimated mass of the vehicle; selects, based at least in part on the first estimated mass, a calibration value using a tuning map; inputs the calibration value, the estimated wheel force, the state estimates, and a measurement noise value to a main mass estimator that calculates a second estimated mass of the vehicle that is more accurate than the first estimated mass due, at least in part, to the calibration value; and adjusts automatically one or more operational parameters of the vehicle based, at least in part, on the second estimated mass.

Methods and systems for estimating the mass of a vehicle

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

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.

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Ann Arbor, MI, United States of America

Shinhoon Kim