BEEP: A Python library for Battery Evaluation and Early Prediction

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...

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Bibliographic Details
Published inSoftwareX Vol. 11; p. 100506
Main Authors 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.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.01.2020
Elsevier
Subjects
Battery
Cycling experiments
Data management
Machine-learning
Python
Data management
Machine-learning
Cycling experiments
Battery
Python
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Summary: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.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2020.100506