BEEP: A Python library for Battery Evaluation and Early Prediction

Patrick K. Herring(Toyota Research Institute), Chirranjeevi Balaji Gopal(Toyota Industries (United States)), Muratahan Aykol(Toyota Industries (United States)), Joseph H. Montoya(Toyota Industries (United States)), Abraham Anapolsky(Toyota Industries (United States)), Peter M. Attia(Stanford University), William E. Gent(Stanford University), Jens S. Hummelshøj(Toyota Industries (United States)), Linda Hung(Toyota Industries (United States)), Ha-Kyung Kwon(Toyota Research Institute), Patrick Moore(Toyota Industries (United States)), Daniel Schweigert(Toyota Research Institute), Kristen Severson(Massachusetts Institute of Technology), Santosh K. Suram(Toyota Research Institute), Zi Yang(Stanford University), Richard D. Braatz(Massachusetts Institute of Technology), Brian D. Storey(Toyota Research Institute)
SoftwareX
January 1, 2020
Cited by 57Open Access
Full Text

Abstract

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.


Related Papers

No related papers found

Powered by citation graph analysis