US 11,768,249 B2
Systems and methods for predicting battery life using data from a diagnostic cycle
William C. Chueh, Stanford, CA (US); Bruis van Vlijmen, San Francisco, CA (US); William E. Gent, Redwood City, CA (US); Vivek Lam, Stanford, CA (US); Patrick K. Herring, Mountain View, CA (US); Chirranjeevi Balaji Gopal, San Jose, CA (US); Patrick A. Asinger, Boston, MA (US); Benben Jiang, Cambridge, MA (US); Richard Dean Braatz, Arlington, MA (US); Xiao Cui, Stanford, CA (US); and Gabriel B. Crane, Stanford, CA (US)
Assigned to Toyota Research Institute, Inc., Los Altos, CA (US); Massachusetts Institute of Technology, Cambridge, MA (US); and The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US)
Filed by Toyota Research Institute, Inc., Los Altos, CA (US); The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US); and Massachusetts Institute of Technology, Cambridge, MA (US)
Filed on Mar. 31, 2021, as Appl. No. 17/218,829.
Claims priority of provisional application 63/107,179, filed on Oct. 29, 2020.
Prior Publication US 2022/0137149 A1, May 5, 2022
Int. Cl. G01R 31/3842 (2019.01); G01R 31/392 (2019.01); G06N 20/00 (2019.01); G01R 31/367 (2019.01); B60L 58/16 (2019.01); G01R 31/36 (2020.01)
CPC G01R 31/3842 (2019.01) [B60L 58/16 (2019.02); G01R 31/367 (2019.01); G01R 31/3648 (2013.01); G01R 31/392 (2019.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A prediction system comprising:
a memory communicably coupled to a processor and storing:
a prediction module including instructions that when executed by the processor cause the processor to:
measure data of a battery cell associated with an electrochemical reaction from a degradation mode having non-linear properties, and the degradation mode is triggered by tests during a diagnostic cycle;
identify a feature that correlates with degradation rates of the battery cell from the data by observations from varying discharge profiles and through grouping the tests according to physical properties, end-of-life (EOL) parameters, a series of chemical properties associated with the observations, and a number of cycles for the battery cell;
predict an EOL of the battery cell by using the feature in a machine learning (ML) model; and
cycle the battery cell according to the EOL for reducing the degradation rates.