A Comparison of Standard SEI Growth Models in the Context of Battery Formation

Growth of the Solid Electrolyte Interphase (SEI) layer on negative electrode particles during the formation cycle is one of the most complex and least understood steps of lithium-ion battery manufacturing. This initial SEI formation significantly impacts battery performance, lifetime, and degradation. Zero-dimensional models, which reduce the complexity of SEI’s morphology, material, and structure, are commonly used to study long-term SEI growth rates and capacity fade. These models are derived based on limiting mechanisms. We aim to compare the most common SEI growth models, focusing on the first few cycles at low C-rates representing formation protocols. Using consistent parameters across models, we seek to understand if they can capture the dynamics of SEI formation. We conducted qualitative comparisons with experimental measurements of Coulombic efficiency in 2032-type coin cells at low C-rate. Our analysis shows that the models predict SEI growth in the first cycle to be higher than in subsequent cycles. However, the difference between cycles in these models is insufficient to explain the experimental results, which indicate a capacity fade during the first cycle that is two orders of magnitude higher than in later cycles. This suggests new models are needed to accurately describe the physics of the formation cycle.

Authors

Institutions

Publication Details

Journal
Journal of The Electrochemical Society
Published
2024-10-01
DOI
https://doi.org/10.1149/1945-7111/ad8548
Citations
12
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
2.50

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Comparison of Standard SEI Growth Models in the Context of Battery Formation

Ferran Brosa Planella, Emma Kendrick, Kawa Manmi, Marcus Tuchel
12 citations
Journal of The Electrochemical Society
Electric Vehicles and Infrastructure
2.50
article

A Comparison of Standard SEI Growth Models in the Context of Battery Formation

Ferran Brosa Planella, Emma Kendrick, Kawa Manmi, Marcus Tuchel
article en
12 citations

Abstract

Growth of the Solid Electrolyte Interphase (SEI) layer on negative electrode particles during the formation cycle is one of the most complex and least understood steps of lithium-ion battery manufacturing. This initial SEI formation significantly impacts battery performance, lifetime, and degradation. Zero-dimensional models, which reduce the complexity of SEI’s morphology, material, and structure, are commonly used to study long-term SEI growth rates and capacity fade. These models are derived based on limiting mechanisms. We aim to compare the most common SEI growth models, focusing on the first few cycles at low C-rates representing formation protocols. Using consistent parameters across models, we seek to understand if they can capture the dynamics of SEI formation. We conducted qualitative comparisons with experimental measurements of Coulombic efficiency in 2032-type coin cells at low C-rate. Our analysis shows that the models predict SEI growth in the first cycle to be higher than in subsequent cycles. However, the difference between cycles in these models is insufficient to explain the experimental results, which indicate a capacity fade during the first cycle that is two orders of magnitude higher than in later cycles. This suggests new models are needed to accurately describe the physics of the formation cycle.

Journal of The Electrochemical SocietyVol. 171(10)
University of Warwick (GB), University of Birmingham (GB)
Faraday Institution
Openalex Percentile: Top 10%
Electric Vehicles and Infrastructure
2.50
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.