The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 24, 2023

Filed:

Jan. 14, 2019
Applicant:

Colorado State University Research Foundation, Fort Collins, CO (US);

Inventors:

Zachary Asher, Kalamazoo, MI (US);

David Baker, Ann Arbor, MI (US);

Thomas H. Bradley, Fort Collins, CO (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
B60W 50/00 (2006.01); B60W 20/10 (2016.01); B60W 30/188 (2012.01); G05B 13/04 (2006.01); H04W 4/02 (2018.01); B60W 10/06 (2006.01); B60W 10/08 (2006.01); B60W 10/10 (2012.01); H04W 4/46 (2018.01); G05D 1/02 (2020.01);
U.S. Cl.
CPC ...
B60W 50/0097 (2013.01); B60W 20/10 (2013.01); B60W 30/188 (2013.01); G05B 13/048 (2013.01); H04W 4/027 (2013.01); H04W 4/46 (2018.02); B60W 10/06 (2013.01); B60W 10/08 (2013.01); B60W 10/10 (2013.01); B60W 2050/0012 (2013.01); B60W 2520/10 (2013.01); B60W 2554/804 (2020.02); B60W 2556/60 (2020.02); B60W 2556/65 (2020.02); B60W 2710/0677 (2013.01); B60W 2710/086 (2013.01); B60W 2720/30 (2013.01); B60Y 2200/92 (2013.01); G05D 1/0278 (2013.01);
Abstract

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.


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