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:
Apr. 12, 2022

Filed:

Oct. 25, 2017
Applicant:

Invently Automotive Inc., Los Altos, CA (US);

Inventor:

Stephen J. Brown, Woodside, CA (US);

Assignee:

INVENTLY AUTOMOTIVE INC., Los Altos, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G01R 31/36 (2020.01); G06F 17/18 (2006.01); G01L 5/13 (2006.01); B60W 30/095 (2012.01); G05D 1/02 (2020.01); G08G 1/01 (2006.01); G05B 23/02 (2006.01); G06N 5/04 (2006.01); G05B 13/04 (2006.01); G06N 20/00 (2019.01); G06Q 10/04 (2012.01); B60W 50/00 (2006.01); G01C 21/34 (2006.01); G01R 31/367 (2019.01); G01L 17/00 (2006.01); G01L 3/26 (2006.01);
U.S. Cl.
CPC ...
G01R 31/3648 (2013.01); B60W 30/0956 (2013.01); B60W 50/0097 (2013.01); G01C 21/3469 (2013.01); G01L 5/13 (2013.01); G05B 13/048 (2013.01); G05B 23/0254 (2013.01); G05D 1/0214 (2013.01); G05D 1/0221 (2013.01); G06F 17/18 (2013.01); G06N 5/048 (2013.01); G06N 20/00 (2019.01); G06Q 10/04 (2013.01); G08G 1/0104 (2013.01); G08G 1/0125 (2013.01); G08G 1/0129 (2013.01); G01L 3/26 (2013.01); G01L 17/00 (2013.01); G01R 31/367 (2019.01);
Abstract

A method for predicting energy consumption of a vehicle using a statistical model. The method includes (i) predicting a set of future input vectors for the vehicle at defined time intervals corresponding to a plurality of future points in time based on a subset of a plurality of reference input vectors previously generated at the defined time intervals at a plurality of previous points in time, (ii) predicting a change in the energy level of the vehicle at the future points in time using a processor and the statistical model, and (iii) providing results corresponding to the predicted change in the energy level to an output unit of the vehicle. The change in the energy level comprises a function of corresponding future input vectors and an associated weighting vector. The change in the energy level is predicted based on a regression analysis of the energy level associated with each of the reference input vectors.


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