dc.contributor.author | Herring, Patrick | |
dc.contributor.author | Balaji Gopal, Chirranjeevi | |
dc.contributor.author | Aykol, Muratahan | |
dc.contributor.author | Montoya, Joseph H | |
dc.contributor.author | Anapolsky, Abraham | |
dc.contributor.author | Attia, Peter M | |
dc.contributor.author | Gent, William | |
dc.contributor.author | Hummelshøj, Jens S | |
dc.contributor.author | Hung, Linda | |
dc.contributor.author | Kwon, Ha-Kyung | |
dc.contributor.author | Moore, Patrick | |
dc.contributor.author | Schweigert, Daniel | |
dc.contributor.author | Severson, Kristen A | |
dc.contributor.author | Suram, Santosh | |
dc.contributor.author | Yang, Zi | |
dc.contributor.author | Braatz, Richard D | |
dc.contributor.author | Storey, Brian D | |
dc.date.accessioned | 2021-10-27T20:22:59Z | |
dc.date.available | 2021-10-27T20:22:59Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/135330 | |
dc.description.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. | |
dc.language.iso | en | |
dc.publisher | Elsevier BV | |
dc.relation.isversionof | 10.1016/J.SOFTX.2020.100506 | |
dc.rights | Creative Commons Attribution 4.0 International license | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Elsevier | |
dc.title | BEEP: A Python library for Battery Evaluation and Early Prediction | |
dc.type | Article | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Materials Science and Engineering | |
dc.relation.journal | SoftwareX | |
dc.eprint.version | Final published version | |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
eprint.status | http://purl.org/eprint/status/PeerReviewed | |
dc.date.updated | 2021-06-09T12:26:11Z | |
dspace.orderedauthors | Herring, P; Balaji Gopal, C; Aykol, M; Montoya, JH; Anapolsky, A; Attia, PM; Gent, W; Hummelshøj, JS; Hung, L; Kwon, H-K; Moore, P; Schweigert, D; Severson, KA; Suram, S; Yang, Z; Braatz, RD; Storey, BD | |
dspace.date.submission | 2021-06-09T12:26:13Z | |
mit.journal.volume | 11 | |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | |