TY  - CONF
AU  - Ali, Haider Adel
AU  - Raijmakers, Luc
AU  - Chayambuka, Kudakwashe
AU  - Danilov, Dmitri
AU  - Notten, Peter H. L.
AU  - Eichel, Rüdiger-A.
TI  - A Hybrid Electrochemical Multi-Particle Model for Li-ion Batteries
M1  - FZJ-2023-03888
PY  - 2023
AB  - 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.
T2  - 244th ECS Meeting
CY  - 8 Oct 2023 - 12 Oct 2023, Gothenburg (Sweden)
Y2  - 8 Oct 2023 - 12 Oct 2023
M2  - Gothenburg, Sweden
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/1016981
ER  -