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Summary & Details

Autonomous Selection of Li-ion Battery Degradation Models Using Capacity Retention Data via Parallel Scenario Computing

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Publication code: 20264527

Paper/Info type: International Journal of Automotive Engineering

Vol.17 No.3

Pages: 119-126(Total 8 p)

Date of publication: Jul 2026

Publisher: JSAE

Language: English

Detailed Information

Category(E)	Research paper
Author(E)	1) Yoichi Takagishi, 2) Tatsuya Yamaue
Affiliation(E)	1) Tohoku University, 2) Kobelco Research Institute Inc.
Abstract(E)	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.

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