Abstract
To sustain the continuous high-rate charge current required for fast charging of electric vehicle batteries, the ionic effective diffusion coefficient of the electrodes must be high enough to avoid the electrode being transport limited. Tortuosity factor and porosity are the two microstructure parameters that control this effective diffusion coefficient. While different methods exist to experimentally measure or calculate the tortuosity factor, no generic relationship between tortuosity and microstructure presently exists that is applicable across a large variety of electrode microstructures and porosities. Indeed, most relationships are microstructure specific. In this work, generic relationships are established using only geometrically defined metrics that can thus be used to design thick electrodes suitable for fast charging. To achieve this objective, an original, discrete particle-size algorithm is introduced and used to identify and segment particles across a set of 19 various electrode microstructures (nickel-manganese-cobalt [NMC] and graphite) obtained from X-ray computed tomography (CT) to quantify parameters such as porosity, particle elongation, sinuosity, and constriction, which influence the effective diffusion coefficient. Compared to the widely used watershed method, the new algorithm shows less over-segmentation. Particle size obtained with different numerical methods is also compared. Lastly, microstructure-Tortuosity relationship and particle size and morphology analysis methods are reviewed.
Original language | American English |
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Article number | 100513 |
Number of pages | 32 |
Journal | Journal of the Electrochemical Society |
Volume | 167 |
Issue number | 10 |
DOIs | |
State | Published - 2020 |
Bibliographical note
Publisher Copyright:© 2020 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited.
NREL Publication Number
- NREL/JA-5400-75813
Keywords
- discrete particle size algorithm
- electrode tortuosity factor
- graphite
- Li-ion battery
- nickel-cobalt-manganese
- particle identification
- pore network