US 12,142,739 B2
Method and system for key predictors and machine learning for configuring cell performance
Sam Keene, Long Beach, CA (US); Giulia Canton, Irvine, CA (US); Ian Browne, Orange, CA (US); Xianyang Li, Irvine, CA (US); Hong Zhao, Aliso Viejo, CA (US); and Benjamin Park, Mission Viejo, CA (US)
Assigned to ENEVATE CORPORATION, Irvine, CA (US)
Filed by Enevate Corporation, Irvine, CA (US)
Filed on Mar. 21, 2022, as Appl. No. 17/699,674.
Application 17/699,674 is a continuation of application No. 17/215,735, filed on Mar. 29, 2021, granted, now 11,283,114.
Application 17/215,735 is a continuation in part of application No. 17/192,877, filed on Mar. 4, 2021, granted, now 11,300,631.
Prior Publication US 2022/0285749 A1, Sep. 8, 2022
Int. Cl. H01M 10/48 (2006.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); H01M 4/02 (2006.01); H01M 4/38 (2006.01); H01M 10/0525 (2010.01)
CPC H01M 10/482 (2013.01) [G06N 5/04 (2013.01); G06N 20/00 (2019.01); H01M 4/386 (2013.01); H01M 10/0525 (2013.01); H01M 2004/027 (2013.01); H01M 2220/20 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method of managing battery performance, the method comprising:
providing a cell comprising a cathode, a separator, and a silicon-dominant anode;
measuring, via a measurement apparatus, one or more parameters relating to operation of the cell, wherein at least one parameter of the one or more parameters is measured during a formation process; and
managing cell performance based on the one or more parameters, wherein the managing comprises assessing the cell performance using a machine learning model.