BEEP: A Python library for Battery Evaluation and Early Prediction
Author(s)
Herring, Patrick; Balaji Gopal, Chirranjeevi; Aykol, Muratahan; Montoya, Joseph H; Anapolsky, Abraham; Attia, Peter M; Gent, William; Hummelshøj, Jens S; Hung, Linda; Kwon, Ha-Kyung; Moore, Patrick; Schweigert, Daniel; Severson, Kristen A; Suram, Santosh; Yang, Zi; Braatz, Richard D; Storey, Brian D; ... Show more Show less
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© 2020 The Authors 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.
Date issued
2020Department
Massachusetts Institute of Technology. Department of Materials Science and EngineeringJournal
SoftwareX
Publisher
Elsevier BV