BEEP: A Python library for Battery Evaluation and Early Prediction
Patrick K. Herring, Chirranjeevi Balaji Gopal, Muratahan Aykol, Joseph H. Montoya, Abraham Anapolsky, Peter M. Attia, William E. Gent, Jens S. Hummelshøj, Linda Hung, Ha-Kyung Kwon, Patrick Moore, Daniel Schweigert, Kristen Severson, Santosh K. Suram, Zi Yang, Richard D. Braatz, Brian D. Storey
Abstract
Battery evaluation and early prediction software package (BEEP) provides an open-source Python-based framework for the management and processing of high-throughput battery cycling data-streams. BEEPs features include file-system based organization of raw cycling data and metadata received from cell testing equipment, validation protocols that ensure the integrity of such data, parsing and structuring of data into Python-objects ready for analytics, featurization of structured cycling data to serve as input for machine-learning, and end-to-end examples that use processed data for anomaly detection and featurized data to train early-prediction models for cycle life. BEEP is developed in response to the software and expertise gap between cell-level battery testing and data-driven battery development.