Colorado State University Research Foundation

Toyota Motor Engineering & Manufacturing North America, Inc.

Zachary Asher

Thomas H Bradley

Joshua D Payne

Benjamin M Geller

Heraldo Felicio Stefanon

Takanori Aoki

David Trinko

David Baker

Embodiments described herein improve fuel economy by controlling a vehicle powertrain based on a predicted vehicle velocity. The vehicle velocity is predicted based on vehicle-to-vehicle data when a prediction horizon is a longer prediction horizon and the vehicle velocity is predicted based on historical drive cycle data when the prediction horizon is a shorter prediction horizon. A time duration of the shorter prediction horizon is shorter than the time duration of the longer prediction horizon. A plurality of drive cycles are established for both the longer and the shorter prediction horizons using a neural network. A shorter prediction horizon drive cycle uses nonlinear autoregressive exogenous model neural networks and the longer prediction horizon drive cycle uses two layer feedforward neural networks. The predicted vehicle velocity is determined from a similar drive cycle of the plurality of drive cycles of either the shorter and/or the longer prediction horizon drive cycles.

Systems and methods for prediction windows for optimal powertrain control

A method may include predicting an acceleration event of a vehicle. The method may further include predicting a maximum speed of the predicted acceleration event. The method may also include determining an engine start time based on the predicted maximum speed. The method may still further include starting an engine of the vehicle at the engine start time.

Systems and methods for determining engine start time during predicted acceleration events

Growing community of inventors

Kalamazoo, MI, United States of America

Zachary Asher