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. 07, 2021

Filed:

Aug. 30, 2018
Applicants:

Feasible, Inc., Emeryville, CA (US);

The Trustees of Princeton University, Princeton, NJ (US);

Inventors:

Daniel A. Steingart, Princeton, NJ (US);

Greg Davies, Plainsboro, NJ (US);

Shaurjo Biswas, El Cerrito, CA (US);

Andrew G. Hsieh, Berkeley, CA (US);

Barry Van Tassell, El Cerrito, CA (US);

Thomas Hodson, Princeton, NJ (US);

Shan Dou, Berkeley, CA (US);

Assignees:

Feasible, Inc., Emeryville, CA (US);

The Trustees of Princeton University, Princeton, NJ (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01R 31/385 (2019.01); G01N 29/07 (2006.01); G01N 29/11 (2006.01); G01N 29/46 (2006.01); G01N 29/12 (2006.01); G01N 29/04 (2006.01); G01R 31/3835 (2019.01); G06N 20/00 (2019.01); G01R 31/392 (2019.01); G01R 31/3842 (2019.01); G01N 29/44 (2006.01); G01N 29/50 (2006.01); H01M 10/42 (2006.01); G01N 29/48 (2006.01); G06N 5/04 (2006.01); H01M 10/48 (2006.01);
U.S. Cl.
CPC ...
G01R 31/385 (2019.01); G01N 29/043 (2013.01); G01N 29/07 (2013.01); G01N 29/11 (2013.01); G01N 29/12 (2013.01); G01N 29/4418 (2013.01); G01N 29/4436 (2013.01); G01N 29/4481 (2013.01); G01N 29/46 (2013.01); G01N 29/48 (2013.01); G01N 29/50 (2013.01); G01R 31/3835 (2019.01); G06N 5/046 (2013.01); G06N 20/00 (2019.01); H01M 10/425 (2013.01); H01M 10/4285 (2013.01); H01M 10/486 (2013.01); G01N 2291/011 (2013.01); G01N 2291/02441 (2013.01); G01N 2291/02827 (2013.01); G01N 2291/02854 (2013.01); G01N 2291/102 (2013.01); G01N 2291/2697 (2013.01); G01R 31/3842 (2019.01); G01R 31/392 (2019.01); H01M 10/48 (2013.01); H01M 2010/4271 (2013.01);
Abstract

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.


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