System and method for using artificial intelligence to detect lithium plating

Abstract

In one aspect, computer-implemented method may include, while a battery pack is charging, receiving, from sensors, measurements associated with the battery pack. The battery pack includes cells. The method may include separating the measurements into separate profiles for the cells, wherein the separate profiles include data pertaining to current, voltage, temperature, or some combination thereof. The method may include identifying, using the separate profiles, features, generating a training dataset by reducing the features based on a mean-comparison technique, a minority scaling technique, or both, and generating a trained machine learning model using the training dataset including the reduced features as labeled input and true lithium plating occurrence statuses as labeled output. The method may include predicting, using the trained machine learning model, an occurrence of lithium plating by inputting subsequently received data into the trained machine learning model.