Methods of Operating Electrochemical Storage Devices Based on Anomaly Clustering, and Software and Systems Including Same

Abstract

Methods of operating electrochemical storage devices, such as secondary batteries, battery modules, and battery cells, using machine-learning models for detecting operating conditions that indicate that one or more electrochemical storages device is/are experiencing an anomaly that may affect its operation. In some embodiments such a method may include deploying an anomaly handler that implements a trained clustering model to identify anomalous operating data and using output of the clustering model to take an operation-control action to control an operation of one or more electrochemical storage devices and/or provide an indication that attention may be needed. In some embodiments a trained detector model is deployed to filter out “normal” operating data so that the trained clustering model handles only “anomalous” operating data, which can drive improvements to the anomaly handler. Methods of training machine-learning models and apparatuses and systems implementing anomaly handlers are also disclosed.

Dependencies are inferred from the claim text.