BEEP: A Python library for Battery Evaluation and Early Prediction
Name
1-s2.0-S2352711020300492-main.pdf
Description
Published version
Size
1.25 MB
Format
Adobe PDF
Checksum (MD5)
3d05c2fed8a6b512baa1e572191491b9
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
Date Issued
2020
Journal
SoftwareX
Publisher
Elsevier BV
Version
Final published version
Abstract
© 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.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.SOFTX.2020.100506
