Autonomous Selection of Li-ion Battery Degradation Models Using Capacity Retention Data via Parallel Scenario Computing
- 提供方法
- 他サイトにて提供・販売
- 入手方法の確認はこちら
- 文献番号
- 20264527
- 文献・情報種別
- International Journal of Automotive Engineering
Vol.17 No.3
- 掲載ページ
- 119-126(Total 8 p)
- 発行年月
- 2026年 7月
- 出版社
- (公社)自動車技術会
- 言語
- 英語
書誌事項
| カテゴリ(英) | Research paper 翻訳 |
|---|---|
| 著者(英) | 1) Yoichi Takagishi, 2) Tatsuya Yamaue |
| 勤務先(英) | 1) Tohoku University, 2) Kobelco Research Institute Inc. |
| 抄録(英) | This study proposes a novel scheme for the simultaneous optimization of degradation parameters and the selection of degradation scenarios for Li-ion battery cells based on capacity retention data. The proposed method identifies the most probable degradation mechanisms through posterior probability. Validation using synthetic data confirmed the scheme's ability to accurately select intended models, such as thin film growth in anode and structural transitions in cathode. Furthermore, the scheme demonstrated robust extrapolation performance, predicting future capacity fade. This framework provides a powerful tool for identifying battery health states and predicting long-term reliability in practical automotive applications. 翻訳 |