Systems and methods for predicting battery life using data from a diagnostic cycle

Patent metadata

Field	Value
Publication number	US-2022137149-A1
Application number	US-202117218829-A
Country	US
Kind code	A1
Filing date	Mar 31, 2021
Priority date	Oct 29, 2020
Publication date	May 5, 2022
Grant date	—

Abstract

System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.

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:
measure electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle;
determine a feature associated with degradation of the battery cell from the electrochemical data; and
predict an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.

2 . The prediction system of claim 1 , wherein the prediction module includes instructions to predict the EOL further including instructions to use the feature in association with satisfying criteria for degradation.

3 . The prediction system of claim 1 , wherein the prediction module further includes instructions to identify the feature according to data from tests for the battery cell at operating conditions that vary to forecast the degradation.

4 . The prediction system of claim 3 , wherein the operating conditions represent an envelope of the battery cell.

5 . The prediction system of claim 3 , wherein the prediction module further includes instructions to group the tests according to parameters related to the EOL of the battery cell and a series of properties associated with the tests.

6 . The prediction system of claim 3 , wherein the prediction module further includes instructions to identify characteristics of the degradation measured from a point or spectrum of the data.

7 . The prediction system of claim 3 , wherein the prediction module further includes instructions to correlate the feature to an energy fade or physical state of the battery cell and train the ML model using the feature.

8 . The prediction system of claim 1 , wherein the feature is related to any one of a rate performance test (RPT), hybrid pulse power characterization (HPPC) resistance, and HPPC relaxation of the battery cell.

9 . The prediction system of claim 1 , wherein the prediction module further includes instructions to determine a degradation condition internal to the battery cell according to the electrochemical reaction.

10 . The prediction system of claim 1 , wherein the EOL is associated with a life span of a battery pack that includes the battery cell.

11 . A non-transitory computer-readable medium comprising:
instructions that when executed by a processor cause the processor to:
measure electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle;
determine a feature associated with degradation of the battery cell from the electrochemical data; and
predict an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.

12 . A method, comprising:
measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle; determining a feature associated with degradation of the battery cell from the electrochemical data; and predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.

13 . The method of claim 12 , wherein predicting the EOL further comprises using the feature in association with satisfying criteria for degradation.

14 . The method of claim 12 , further comprising:
identifying the feature according to data from tests for the battery cell at operating conditions that vary to forecast the degradation.

15 . The method of claim 14 , wherein the operating conditions represent an envelope of the battery cell.

16 . The method of claim 14 , further comprising:
grouping the tests according to parameters related to the EOL of the battery cell and a series of properties associated with the tests.

17 . The method of claim 14 , further comprising:
identifying characteristics of the degradation measured from a point or spectrum of the data.

18 . The method of claim 14 , further comprising:
correlating the feature to an energy fade or physical state of the battery cell; and training the ML model using the feature.

19 . The method of claim 12 , wherein the feature is related to any one of a rate performance test (RPT), hybrid pulse power characterization (HPPC) resistance, and HPPC relaxation of the battery cell.

20 . The method of claim 12 , further comprising:
determining a degradation condition internal to the battery cell according to the electrochemical reaction.

Assignees

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

Inventors

• Chueh William C
• Van Vlijmen Bruis
• Gent William E
• Lam Vivek
• Herring Patrick K
• Balaji Gopal Chirranjeevi
• Asinger Patrick A
• Jiang Benben
• Braatz Richard Dean
• Cui Xiao
• Crane Gabriel B

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/0985

  Hyperparameter optimisation; Meta-learning; Learning-to-learn · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title