GalvAnalyze: Streamlining Data Analysis of Galvanostatic Battery Cycling

McNulty, Rory C.; Rier, Lukas

Abstract

We present GalvAnalyze, a software tool developed in Python, that processes data collected using a variety of battery cyclers and creates a set of outputs that greatly reduce the inefficiencies associated with manual or semi-manual analysis of galvanostatic cycling data. An experiment is carried out by 10 participants with varying degrees of experience processing galvanostatic cycling data, enabling quantitative analysis of the processing time benefit that GalvAnalyze enables. The functionality of GalvAnalyze includes handling data where the applied current density varies, separating the data into individual charge-discharge cycle pairs, and producing hysteresis plots using a simple graphical user interface. The executable can be downloaded at https://www.thenamilab.com/ and is built to be accessible, with no prior coding expertise required. In the interest of transparency, and to allow future contributions to the functionality of GalvAnalyze by the wider community, the source code can be found on GitHub (https://github.com/LukasRier/GalvAnalyze).

• Journal Article Type: Article
• Acceptance Date: May 17, 2023
• Online Publication Date: May 17, 2023
• Deposit Date: May 18, 2023
• Publicly Available Date: May 17, 2023
• Journal: Batteries & Supercaps
• Print ISSN: 2566-6223
• Electronic ISSN: 2566-6223
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1002/batt.202300038
• Keywords: Batteries; Lithium-ion; Data Processing; Python; Open-source
• Public URL: https://nottingham-repository.worktribe.com/output/20836908
• Publisher URL: https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/batt.202300038