# Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy for Lithium Metal Battery Degradation via Machine Learning

https://mdr.nims.go.jp/datasets/177c48ef-038c-47a8-8061-ef444746d444

## File

- [Advanced Science - 2025 - Si - Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy.pdf](https://mdr.nims.go.jp/filesets/6d26afbe-e8b5-40c9-9e36-637cff9ea8ad/download) ([Detail](https://mdr.nims.go.jp/filesets/6d26afbe-e8b5-40c9-9e36-637cff9ea8ad.md))

## Id

177c48ef-038c-47a8-8061-ef444746d444

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-09-08T22:52:57.558110Z

## Updated at

2025-09-09T03:30:20.999776Z

## Published at

2025-09-09T03:19:03.997903Z

## Doi



## First published url

https://doi.org/10.1002/advs.202502336

## Date published

2025-05-05

## Recorded date published

2025-7

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance
    Spectroscopy for Lithium Metal Battery Degradation via Machine Learning
  title_type: original
  lang: en

## Description

- description: 'Lithium-metal batteries (LMBs) are emerging as a promising next-generation
    energy storage due to their exceptionally high energy density. However, accurately
    predicting their performance remains challenging because of the complex degradation
    mechanisms. In this study, a machine learning (ML) framework is proposed that
    combines electrochemical impedance spectroscopy (EIS) with the XGBoost algorithm
    to develop two predictive models: one for estimating capacity degradation and
    another for detecting the knee point (KP)—a critical inflection point in the degradation
    trajectory. SHapley Additive exPlanations (SHAP) analysis is employed to interpret
    feature importance, revealing that low-frequency imaginary impedance components—associated
    with diffusion-limited processes such as lithium depletion and accumulation—are
    most influential for capacity estimation. Conversely, high-frequency features
    related to charge transfer resistance play a dominant role in the KP detection.
    To reduce data complexity and improve model efficiency, the input by selecting
    specific frequency points based on SHAP values is further optimized. The optimized
    models exhibit comparable or improved accuracy compared to those using the whole
    EIS data and have reasonable performance on unseen test data. The findings highlight
    that EIS-based ML models can accurately forecast heaslth of LMBs, providing deeper
    insights into their aging processes and enhancing battery management strategies.'
  description_type: abstract
  lang: und

## Creator

- name: Qianli Si
  role: author
  organization: National Institute for Materials Science
- name: Shoichi Matsuda
  role: author
  orcid: https://orcid.org/0000-0002-0640-3404
  organization: National Institute for Materials Science
- name: Yasunobu Ando
  role: author
- name: Toshiyuki Momma
  role: author
- name: Yoshitaka Tateyama
  role: author
  orcid: https://orcid.org/0000-0002-5532-6134
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Wiley

## Managing organization



## Keyword

- subject: lithium metal battery
  schema: not_defined
- subject: machine learning
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Advanced Science
  issn: '21983844'
  volume: '12'
  issue: '27'
  article_number: '2502336'

## Conference



## Related item



## Funding

- identifier: JPMXP1020230327
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: JPMXP112271280
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: JPMJPF2016
  funder_name: Japan Science and Technology Agency
- identifier: JPMXP1020230325
  funder_name: Ministry of Education, Culture, Sports, Science and Technology

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 6d26afbe-e8b5-40c9-9e36-637cff9ea8ad
  filename: Advanced Science - 2025 - Si - Capacity Estimation and Knee Point Prediction
    Using Electrochemical Impedance Spectroscopy.pdf
  content_type: application/pdf
  size: 1865186
  md5: b47bcbfec896c66ac949f92fa8bbcaeb

## Thumbnail

fileset_id: 6d26afbe-e8b5-40c9-9e36-637cff9ea8ad
filename: Advanced Science - 2025 - Si - Capacity Estimation and Knee Point Prediction
  Using Electrochemical Impedance Spectroscopy.pdf