Effect of the Particle Size Averaging Method on the Predicted Behavior of Lithium-ion Batteries According to the Pseudo-2D Model

The pseudo-2D (P2D) model is widely utilized in the modeling of lithium-ion batteries for product design and state monitoring applications. The P2D model relies on an average electrode particle size as an input parameter, but existing literature has been inconsistent as to how this is obtained. In this work, we examined the effect of the particle size averaging method on the behavior of Li-ion batteries as predicted by the P2D model. We first set up a standard P2D model that only considers representative sizes and a modified P2D model that accounts for the entire size distribution. Number average, volume-surface average, and D50 particle sizes were then obtained for graphite anodes and lithium iron phosphate and nickel manganese cobalt oxide cathodes typically found in commercial lithium-ion cells. Lastly, the results generated by monodisperse and polydisperse models were compared. It was found that using the volume-surface average and D50 sizes result in accurate predictions of the energy density and cell capacity with respect to the polydisperse model, while using the number average particle size may result in notable discrepancies. For this reason, the number average particle size, or getting a simple average particle size from scanning electron microscopy images, should be avoided.

Authors

Institutions

Publication Details

Journal
Journal of The Electrochemical Society
Published
2024-12-03
DOI
https://doi.org/10.1149/1945-7111/ad9d80
Citations
3
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.61
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Effect of the Particle Size Averaging Method on the Predicted Behavior of Lithium-ion Batteries According to the Pseudo-2D Model

Michael T. Castro, Joey D. Ocon
3 citations
Journal of The Electrochemical Society
Advanced Battery Technologies Research
0.61
article

Effect of the Particle Size Averaging Method on the Predicted Behavior of Lithium-ion Batteries According to the Pseudo-2D Model

Michael T. Castro, Joey D. Ocon
article en
3 citations

Abstract

The pseudo-2D (P2D) model is widely utilized in the modeling of lithium-ion batteries for product design and state monitoring applications. The P2D model relies on an average electrode particle size as an input parameter, but existing literature has been inconsistent as to how this is obtained. In this work, we examined the effect of the particle size averaging method on the behavior of Li-ion batteries as predicted by the P2D model. We first set up a standard P2D model that only considers representative sizes and a modified P2D model that accounts for the entire size distribution. Number average, volume-surface average, and D50 particle sizes were then obtained for graphite anodes and lithium iron phosphate and nickel manganese cobalt oxide cathodes typically found in commercial lithium-ion cells. Lastly, the results generated by monodisperse and polydisperse models were compared. It was found that using the volume-surface average and D50 sizes result in accurate predictions of the energy density and cell capacity with respect to the polydisperse model, while using the number average particle size may result in notable discrepancies. For this reason, the number average particle size, or getting a simple average particle size from scanning electron microscopy images, should be avoided.

Journal of The Electrochemical SocietyVol. 171(12)
University of the Philippines Diliman (PH)
Affordable and clean energy
Openalex Percentile: Top 32%
Advanced Battery Technologies Research
0.61
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.