# Data‐Driven Cycle Life Prediction of Lithium Metal‐Based Rechargeable Battery Based on Discharge/Charge Capacity and Relaxation Features

https://mdr.nims.go.jp/datasets/ca5f2d26-d0c5-41a8-9131-2afbf4ce84b4

## File

- [Advanced Science - 2024 - Si - Data‐Driven Cycle Life Prediction of Lithium Metal‐Based Rechargeable Battery Based on.pdf](https://mdr.nims.go.jp/filesets/2b5fec37-e6bd-47d8-a498-aef41b642805/download) ([Detail](https://mdr.nims.go.jp/filesets/2b5fec37-e6bd-47d8-a498-aef41b642805.md))

## Id

ca5f2d26-d0c5-41a8-9131-2afbf4ce84b4

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-10-23T19:16:00.945167Z

## Updated at

2024-11-22T07:30:54.127398Z

## Published at

2024-11-22T07:30:54.201807Z

## Doi



## First published url

https://doi.org/10.1002/advs.202402608

## Date published

2024-06-27

## Recorded date published

2024-9

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Data‐Driven Cycle Life Prediction of Lithium Metal‐Based Rechargeable Battery
    Based on Discharge/Charge Capacity and Relaxation Features
  title_type: original
  lang: en

## Description

- description: Achieving precise estimates of battery cycle life is a formidable challenge
    due to the nonlinear nature of battery degradation. This study explores an approach
    using machine learning (ML) methods to predict the cycle life of lithium-metal-based
    rechargeable batteries with high mass loading LiNi0.8Mn0.1Co0.1O2 electrode, which
    exhibits more complicated and electrochemical profile during battery operating
    conditions than typically studied LiFePO₄/graphite based rechargeable batteries.
    Extracting diverse features from discharge, charge, and relaxation processes,
    the intricacies of cell behavior without relying on specific degradation mechanisms
    are navigated. The best-performing ML model, after feature selection, achieves
    an R2 of 0.89, showcasing the application of ML in accurately forecasting cycle
    life. Feature importance analysis unveils the logarithm of the minimum value of
    discharge capacity difference between 100 and 10 cycle (Log(|min(ΔDQ 100–10(V))|))
    as the most important feature. Despite the inherent challenges, this model demonstrates
    a remarkable 6.6% test error on unseen data, underscoring its robustness and potential
    for transformative advancements in battery management systems. This study contributes
    to the successful application of ML in the realm of cycle life prediction for
    lithium-metal-based rechargeable batteries with practically high energy density
    design.
  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: Youhei Yamaji
  role: author
  orcid: https://orcid.org/0000-0002-4055-8792
  organization: National Institute for Materials Science
- 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

## 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: '11'
  issue: '33'
  article_number: '2402608'

## Conference



## Related item



## Funding



## 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



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## Custom property



## Fileset

- id: 2b5fec37-e6bd-47d8-a498-aef41b642805
  filename: Advanced Science - 2024 - Si - Data‐Driven Cycle Life Prediction of Lithium
    Metal‐Based Rechargeable Battery Based on.pdf
  content_type: application/pdf
  size: 3959163
  md5: 9bfb321ed50ee4900e08e4a66df9c348

## Thumbnail

fileset_id: 2b5fec37-e6bd-47d8-a498-aef41b642805
filename: Advanced Science - 2024 - Si - Data‐Driven Cycle Life Prediction of Lithium
  Metal‐Based Rechargeable Battery Based on.pdf