BEEP: A Python library for Battery Evaluation and Early Prediction
P. Herring, C. Balaji Gopal, M. Aykol, J.H. Montoya, A. Anapolsky, P.M. Attia, W. Gent, J.S. Hummelshøj, L. Hung, H.-K. Kwon, P. Moore, D. Schweigert, K.A. Severson, S. Suram, Z. Yang, R.D. Braatz, B.D. Storey
SoftwareX 11 (2020) 100506. https://doi.org/10.1016/j.softx.2020.100506
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.
