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:
May. 03, 2016

Filed:

Sep. 24, 2013
Applicant:

GM Global Technology Operations Llc, Detroit, MI (US);

Inventors:

John X. Cui, Rochester Hills, MI (US);

Min Sun, Troy, MI (US);

Anthony B. Will, Macomb Township, MI (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
F01N 3/02 (2006.01); F01N 11/00 (2006.01); F01N 3/023 (2006.01); F02D 41/14 (2006.01); F02D 41/24 (2006.01); F02D 41/02 (2006.01); F01N 9/00 (2006.01);
U.S. Cl.
CPC ...
F01N 11/00 (2013.01); F01N 3/023 (2013.01); F01N 9/005 (2013.01); F01N 11/002 (2013.01); F02D 41/1405 (2013.01); F02D 41/1448 (2013.01); F02D 41/2432 (2013.01); F01N 9/002 (2013.01); F01N 2900/08 (2013.01); F01N 2900/1606 (2013.01); F02D 41/029 (2013.01); F02D 41/1467 (2013.01); F02D 2200/0812 (2013.01);
Abstract

A method of estimating soot loading in a diesel particulate filter (DPF) in a vehicle exhaust system includes estimating an engine-out soot rate using a first neural network that has a first set of vehicle operating conditions as inputs. The method further includes estimating DPF soot loading using a second neural network that has the estimated engine-out soot rate from the first neural network and a second set of vehicle operating conditions as inputs. Estimating the engine-out soot rate and estimating the DPF soot loading are performed by an electronic controller that executes the first and the second neural networks. The method also provides for training the first and second neural networks both offline (for initial settings of the neural networks in the vehicle), and online (when the vehicle is being used by a vehicle operator). An exhaust system has a controller that implements the method.


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