@techreport{01cfe2b8cb8f4b3980c8cdcbe95122a5,
title = "Combining multi-modal non-destructive techniques to investigate ageing and orientation effects in automotive Li-ion pouch cells",
abstract = "As the electrification of the transport sector progresses, an abundance of lithium-ion batteries inside electric vehicles (EVs) will reach their end-of-life (EoL). The cells inside battery packs will age differently depending on multiple factors during their use. Currently, there is limited publicly available research on the degradation of the individual cells recovered from real-world EV usage. Once they have been recovered from the vehicle, large-format pouch cells are challenging to characterise, measure their internal structure and determine state-of-health (SoH). Here, large-format (261 x 216 x 7.91 mm) Nissan Leaf cells are harvested from an EV and four complementary non-destructive techniques are used to distinguish the ageing of cells arranged in varying orientations and locations within the pack. The measurement suite includes infrared thermography, ultrasonic mapping, X-ray computed tomography, and synchrotron X-ray diffraction, and represents a unique combination of characterisation techniques. We found that each of the non-destructive diagnostic techniques corroborated each other yet provide different complementary insights. The influence of orientation and location of the cells is significant, with the rotated/vertically aligned cells differing significantly from the flat/horizontally aligned cells in mode and degree of ageing. These insights provide new information on cell degradation that can help to influence pack design and illustrates how rapid and relatively inexpensive technology can provide sufficient information for practical assessment compared to costly synchrotron studies. Such an approach can inform decision support at EoL and more efficient battery production reducing the wastage of raw materials.",
keywords = "Automotive Li-ion pouch cell, electric vehicle, ageing, infrared thermal imaging, ultrasound, acoustic measurement, X-ray tomography, deep learning, synchrotron X-ray diffraction",
author = "Arthur Fordham and Zoran Milojevic and Emily Giles and Wenjia Du and Owen, {Rhodri E.} and Stefan Michalik and Philip Chater and Prodip Das and Pierrot Attidekou and Lambert, {Simon M.} and Phoebe Allan and Peter Slater and Paul Anderson and Rhodri Jervis and Shearing, {Paul R.} and Brett, {Dan J. L.}",
year = "2023",
month = may,
day = "10",
doi = "10.26434/chemrxiv-2023-cghv4",
language = "English",
publisher = "ChemRxiv",
type = "WorkingPaper",
institution = "ChemRxiv",
}