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.
Patent No.:
Date of Patent:
Jan. 18, 2022
Filed:
Oct. 16, 2018
The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US);
Massachusetts Institute of Technology, Cambridge, MA (US);
Kristen Ann Severson, Cambridge, MA (US);
Richard Dean Braatz, Arlington, MA (US);
William C. Chueh, Menlo Park, CA (US);
Peter M. Attia, Stanford, CA (US);
Norman Jin, Palo Alto, CA (US);
Stephen J. Harris, Walnut Creek, CA (US);
Nicholas Perkins, San Francisco, CA (US);
The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US);
Massachusetts Institute of Technology, Cambridge, MA (US);
Abstract
A method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ΔQ(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.