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:
Dec. 20, 2022

Filed:

Jan. 24, 2020
Applicant:

Toyota Research Institute, Inc., Los Altos, CA (US);

Inventors:

Patrick K. Herring, Mountain View, CA (US);

Chirranjeevi Balaji Gopal, San Jose, CA (US);

Abraham S. Anapolsky, San Mateo, CA (US);

Assignee:

Toyota Research Institute, Inc., Los Altos, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
B60L 58/16 (2019.01); G06N 20/00 (2019.01); G06N 5/04 (2006.01); G07C 5/00 (2006.01); H01M 10/42 (2006.01); H01M 10/48 (2006.01); G07C 5/02 (2006.01);
U.S. Cl.
CPC ...
B60L 58/16 (2019.02); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G07C 5/008 (2013.01); G07C 5/02 (2013.01); H01M 10/425 (2013.01); H01M 10/482 (2013.01); H01M 2010/4271 (2013.01); H01M 2220/20 (2013.01);
Abstract

A battery management system includes one or more processors, a battery comprising a plurality of cells, an output device, an input device, and a memory having an input module, a battery characteristic prediction module, and an output module. The input module includes instructions that cause the one or more processors to receive a mode selection from a user via the input device. The battery characteristic prediction module includes instructions that cause the one or more processors to predict a characteristic of the battery based on the mode selection using an active machine learning model to predict the characteristic of the battery. The output module includes instructions that cause the one or more processors to output an estimated cost to the output device based on the characteristic of the battery determined by the active machine learning model.


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