Citation

BibTex format

@article{Kench:2024:10.1016/j.matt.2024.08.014,
author = {Kench, S and Squires, I and Dahari, A and Brosa, Planella F and Roberts, SA and Cooper, SJ},
doi = {10.1016/j.matt.2024.08.014},
journal = {Matter},
pages = {4260--4269},
title = {Li-ion battery design through microstructural optimization using generative AI},
url = {http://dx.doi.org/10.1016/j.matt.2024.08.014},
volume = {7},
year = {2024}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Lithium-ion batteries are used across various applications, necessitating tailored cell designs to enhance performance. Optimizing electrode manufacturing parameters is a key route to achieving this, as these parameters directly influence the microstructure and performance of the cells. However, linking process parameters to performance is complex, and experimental or modeling campaigns are often slow and expensive. This study introduces a fast computational optimization framework for electrode manufacturing parameters. A generative model, trained on a small dataset of microstructural images associated with different manufacturing parameters, efficiently generates representative microstructures for new parameters. This model is integrated into a Bayesian optimization loop that includes microstructure generation, characterization, and simulation, aiming to find optimal manufacturing parameters for a particular application. Significant improvement in the energy density of a 4680 cell is achieved through bespoke cell design, highlighting the importance of cell-scale normalization. The framework's modularity allows its application to various advanced materials manufacturing scenarios.
AU - Kench,S
AU - Squires,I
AU - Dahari,A
AU - Brosa,Planella F
AU - Roberts,SA
AU - Cooper,SJ
DO - 10.1016/j.matt.2024.08.014
EP - 4269
PY - 2024///
SN - 2590-2393
SP - 4260
TI - Li-ion battery design through microstructural optimization using generative AI
T2 - Matter
UR - http://dx.doi.org/10.1016/j.matt.2024.08.014
VL - 7
ER -

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