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@INPROCEEDINGS{Ali:1016981,
author = {Ali, Haider Adel and Raijmakers, Luc and Chayambuka,
Kudakwashe and Danilov, Dmitri and Notten, Peter H. L. and
Eichel, Rüdiger-A.},
title = {{A} {H}ybrid {E}lectrochemical {M}ulti-{P}article {M}odel
for {L}i-ion {B}atteries},
reportid = {FZJ-2023-03888},
year = {2023},
abstract = {Physics-based models have proven to be effective tools for
understanding the behavior of Li-ion batteries, which is
essential for improving their design and performance. Among
the various physics-based models, the Doyle-Fuller-Newman
(DFN) model has emerged as the most widely used due to its
accurate simulation of battery behavior. To address certain
limitations, the Multiple-Particle DFN (MP-DFN) model was
introduced. The MP-DFN model employs multiple electrode
particle sizes to account for internal concentration
heterogeneities and accurately capture slow diffusion
processes. However, it is worth noting that the MP-DFN model
comes with a relatively high computational cost. To overcome
these challenges, this study has developed a
Hybrid-Multiple-Particle DFN (HMP-DFN) model.},
month = {Oct},
date = {2023-10-08},
organization = {244th ECS Meeting, Gothenburg
(Sweden), 8 Oct 2023 - 12 Oct 2023},
subtyp = {After Call},
cin = {IEK-9},
cid = {I:(DE-Juel1)IEK-9-20110218},
pnm = {1223 - Batteries in Application (POF4-122) / LLEC::VxG -
Integration von "Vehicle-to-grid" (BMBF-03SF0628)},
pid = {G:(DE-HGF)POF4-1223 / G:(DE-Juel1)BMBF-03SF0628},
typ = {PUB:(DE-HGF)24},
url = {https://juser.fz-juelich.de/record/1016981},
}