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

A non-transitory computer-readable medium comprising:

• instructions that when executed by a 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 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.

A method, comprising:

• measuring 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;
• identifying 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;
• predicting an EOL of the battery cell by using the feature in a machine learning (ML) model;
• and
• cycling the battery cell according to the EOL for reducing the degradation rates.