BEEP: A Python library for Battery Evaluation and Early Prediction
© 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...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/135330 |
| _version_ | 1826194530769043456 |
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| author | 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 |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering 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 |
| author_sort | Herring, Patrick |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T09:57:31Z |
| format | Article |
| id | mit-1721.1/135330 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:57:31Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1353302023-02-23T15:05:15Z BEEP: A Python library for Battery Evaluation and Early Prediction 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 Massachusetts Institute of Technology. Department of Materials Science and Engineering © 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. 2021-10-27T20:22:59Z 2021-10-27T20:22:59Z 2020 2021-06-09T12:26:11Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135330 en 10.1016/J.SOFTX.2020.100506 SoftwareX Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Elsevier BV Elsevier |
| spellingShingle | 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 BEEP: A Python library for Battery Evaluation and Early Prediction |
| title | BEEP: A Python library for Battery Evaluation and Early Prediction |
| title_full | BEEP: A Python library for Battery Evaluation and Early Prediction |
| title_fullStr | BEEP: A Python library for Battery Evaluation and Early Prediction |
| title_full_unstemmed | BEEP: A Python library for Battery Evaluation and Early Prediction |
| title_short | BEEP: A Python library for Battery Evaluation and Early Prediction |
| title_sort | beep a python library for battery evaluation and early prediction |
| url | https://hdl.handle.net/1721.1/135330 |
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