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

Qianli Si (National Institute for Materials Science) ; Shoichi Matsuda SAMURAI ORCID (National Institute for Materials Science) ; Youhei Yamaji SAMURAI ORCID (National Institute for Materials Science) ; Toshiyuki Momma ; Yoshitaka Tateyama SAMURAI ORCID (National Institute for Materials Science)

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Qianli Si, Shoichi Matsuda, Youhei Yamaji, Toshiyuki Momma, Yoshitaka Tateyama. Data‐Driven Cycle Life Prediction of Lithium Metal‐Based Rechargeable Battery Based on Discharge/Charge Capacity and Relaxation Features. Advanced Science. 2024, 11 (33), 2402608. https://doi.org/10.1002/advs.202402608
SAMURAI

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(abstract)

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.

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Keyword: lithium metal battery

Date published: 2024-06-27

Publisher: Wiley

Journal:

  • Advanced Science (ISSN: 21983844) vol. 11 issue. 33 2402608

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Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.1002/advs.202402608

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Updated at: 2024-11-22 16:30:54 +0900

Published on MDR: 2024-11-22 16:30:54 +0900