Systems and methods for machine learning enabled fault detection in rechargeable batteries

Abstract

In one aspect, computer-implemented method may include receiving, from one or more sensors associated with a battery pack, one or more measurements pertaining to voltage, temperature, or both. The method may include transforming the one or more measurements into a time-series sequential window format, determining, based on the time-series sequential window format of the one or more measurements, a voltage score and a temperature score, and predicting, based on the voltage score and the temperature score, whether the battery pack is experiencing a fault condition. The prediction is performed by one or more trained machine learning models. Responsive to predicting the battery pack is experiencing the fault condition, the method may include performing one or more preventative actions.