SoftwareX (Jan 2020)

BEEP: A Python library for Battery Evaluation and Early Prediction

  • Patrick Herring,
  • Chirranjeevi Balaji Gopal,
  • Muratahan Aykol,
  • Joseph H. Montoya,
  • Abraham Anapolsky,
  • Peter M. Attia,
  • William Gent,
  • Jens S. Hummelshøj,
  • Linda Hung,
  • Ha-Kyung Kwon,
  • Patrick Moore,
  • Daniel Schweigert,
  • Kristen A. Severson,
  • Santosh Suram,
  • Zi Yang,
  • Richard D. Braatz,
  • Brian D. Storey

Journal volume & issue
Vol. 11

Abstract

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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.

Keywords