Systems and methods for predicting the cycle life of cycling protocols

Patent metadata

Field	Value
Publication number	US-2022341995-A1
Application number	US-202117235356-A
Country	US
Kind code	A1
Filing date	Apr 20, 2021
Priority date	Apr 20, 2021
Publication date	Oct 27, 2022
Grant date	—

Abstract

System, methods, and other embodiments described herein relate to improving the cycling of batteries by using data and a hierarchical Bayesian model (HBM) for predicting the cycle life of a cycling protocol. In one embodiment, a method includes classifying cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols. The method also includes quantifying, using the class in a HBM, variability for the battery induced by the protocol. The method also includes predicting, using the HBM, an adjusted cycle life for the protocol according to the variability. The method also includes communicating the adjusted cycle life to operate the battery.

First claim

What is claimed is:

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: classify cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols; quantify, using the class in a hierarchical Bayesian model (HBM), variability for the battery induced by the protocol; predict, using the HBM, an adjusted cycle life for the protocol according to the variability; and communicate the adjusted cycle life to operate the battery.

2 . The prediction system of claim 1 , wherein the prediction module further includes instructions to adjust, using the HBM, the cycle life according to the cycle life distributions, battery variability measured by the cycling protocols, and total cycles distributions of the cycling protocols to infer the adjusted cycle life.

3 . The prediction system of claim 2 , wherein the cycle life distributions is a level of the HBM and the battery variability and the total cycles distributions are another level of the HBM.

4 . The prediction system of claim 1 , wherein the prediction module further includes instructions to adjust, using the HBM, the cycle life according to prior expectations of the cycling protocols for performance associated with various batteries.

5 . The prediction system of claim 1 , wherein the prediction module further includes instructions to cycle the battery using the protocol when the adjusted cycle life satisfies criteria for total cycles until degradation.

6 . The prediction system of claim 1 , wherein the prediction module includes instructions to classify the battery further including instructions to determine, using a prediction model, the class according to constructed features using other data from electrochemical cycling of the battery.

7 . The prediction system of claim 1 , wherein the prediction module further includes instructions to determine whether a confidence of the adjusted cycle life satisfies criteria for the cycling protocols.

8 . The prediction system of claim 1 , wherein the cycle life represents cycles until a charge level of the battery degrades and the cycling protocols represent charging protocols for the battery.

9 . A non-transitory computer-readable medium comprising:
instructions that when executed by a processor cause the processor to: classify cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols; quantify, using the class in a hierarchical Bayesian model (HBM), variability for the battery induced by the protocol; predict, using the HBM, an adjusted cycle life for the protocol according to the variability; and communicate the adjusted cycle life to operate the battery.

10 . The non-transitory computer-readable medium of claim 9 , further including instructions that when executed by the processor cause the processor to adjust, using the HBM, the cycle life according to the cycle life distributions, battery variability measured by the cycling protocols, and total cycles distributions of the cycling protocols to infer the adjusted cycle life.

11 . The non-transitory computer-readable medium of claim 10 , wherein the cycle life distributions is a level of the HBM and the battery variability and the total cycles distributions are another level of the HBM.

12 . The non-transitory computer-readable medium of claim 9 , wherein the instructions to classify the battery further include instructions to determine, using a prediction model, the class according to constructed features using other data from electrochemical cycling of the battery.

13 . A method, comprising:
classifying cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols; quantifying, using the class in a hierarchical Bayesian model (HBM), variability for the battery induced by the protocol; predicting, using the HBM, an adjusted cycle life for the protocol according to the variability; and communicating the adjusted cycle life to operate the battery.

14 . The method of claim 13 , further comprising:
adjusting, using the HBM, the cycle life according to the cycle life distributions, battery variability measured by the cycling protocols, and total cycles distributions of the cycling protocols to infer the adjusted cycle life.

15 . The method of claim 14 , wherein the cycle life distributions is a level of the HBM and the battery variability and the total cycles distributions are another level of the HBM.

16 . The method of claim 13 , further comprising:
adjusting, using the HBM, the cycle life according to prior expectations of the cycling protocols for performance associated with various batteries.

17 . The method of claim 13 , further comprising:
cycling the battery using the protocol when the adjusted cycle life satisfies criteria for total cycles until degradation.

18 . The method of claim 13 , wherein classifying of the battery includes determining, using a prediction model, the class according to constructed features using other data from electrochemical cycling of the battery.

19 . The method of claim 13 , further comprises:
determining whether a confidence of the adjusted cycle life satisfies criteria for the cycling protocols.

20 . The method of claim 13 , wherein the cycle life represents cycles until a charge level of the battery degrades and the cycling protocols represent charging protocols for the battery.

Assignees

• Toyota Res Inst Inc
• Univ Leland Stanford Junior
• Massachusetts Inst Technology

Inventors

• Braatz Richard Dean
• Jiang Benben
• Mohr Fabian
• Forsuelo Michael
• Gent William E
• Herring Patrick K
• Chueh William C
• Harris Stephen J

Classifications

• G01R31/392 Primary

  Determining battery ageing or deterioration, e.g. state of health · CPC title

• Y02E60/10

  Energy storage using batteries · CPC title

• H01M2220/20

  Batteries in motive systems, e.g. vehicle, ship, plane · CPC title

• H01M10/42

  Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells (H01M10/60 takes precedence) · CPC title

• G01R31/367 Primary

  Software therefor, e.g. for battery testing using modelling or look-up tables · CPC title