Info: Zenodo’s user support line is staffed on regular business days between Dec 23 and Jan 5. Response times may be slightly longer than normal.

Published November 26, 2023 | Version v1
Dataset Open

Underlying dataset for battery pack degradation - Understanding aging in parallel-connected lithium-ion batteries under thermal gradients

  • 1. ROR icon Imperial College London

Contributors

  • 1. ROR icon Imperial College London

Description

This record constitutes the raw data underlying the paper "Battery pack degradation - Understanding aging in parallel-connected lithium-ion batteries under thermal gradients" (preprint link)

The dataset contains all raw data, processed data and analysis codes used to generate figures in the publication. Abstract is as follows:

Practical lithium-ion battery systems require parallelisation of tens to hundreds of cells, however understanding of how pack-level thermal gradients influence lifetime performance  remains a research gap. Here we present an experimental study of surface cooled parallel-string battery packs (temperature range 20-45 °C), and identify two main operational modes; convergent degradation with homogeneous temperatures, and (the more detrimental) divergent degradation driven by thermal gradients. We attribute the divergent case to the, often overlooked, cathode impedance growth. This was negatively correlated with temperature and can cause positive feedback where the impedance of cells in parallel diverge over time; increasing heterogeneous current and state-of-charge distributions. These conclusions are supported by current distribution measurements, decoupled impedance measurements and degradation mode analysis. From this, mechanistic explanations are proposed, alongside a publicly available aging dataset, which highlights the critical role of capturing cathode degradation in parallel-connected batteries; a key insight for battery pack developers.

Files

Cycling_Characterisation.zip

Files (17.3 GB)

Name Size Download all
md5:a3d66e6a1406bc52e00e090e5dd096f2
817.6 MB Preview Download
md5:61dcfb344922bde7615a1c8cb293fed8
257.4 MB Preview Download
md5:fdfa4e401bef6bc800167dd7a3b82531
20.1 MB Preview Download
md5:6418e91f6edcbc993dafdd6346fe5c7d
16.2 GB Preview Download
md5:eae01f915f6349a8b9a1eb80f955f2dd
1.9 kB Preview Download

Additional details

Related works

Is published in
Preprint: 10.21203/rs.3.rs-2535223/v1 (DOI)
Thesis: 10.25560/92251 (DOI)

Dates

Collected
2020-02/2021-01
Data collected