Accelerated commercial battery electrode-level degradation diagnosis via only 11-point charging segments
Accelerated and accurate degradation diagnosis is imperative for the management and reutilization of commercial lithium-ion batteries in the upcoming TWh era. Different from traditional methods, this work proposes a hybrid framework for rapid and accurate degradation diagnosis at the electrode level...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2025-01-01
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| Series: | eScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667141724001241 |
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| Summary: | Accelerated and accurate degradation diagnosis is imperative for the management and reutilization of commercial lithium-ion batteries in the upcoming TWh era. Different from traditional methods, this work proposes a hybrid framework for rapid and accurate degradation diagnosis at the electrode level combining both deep learning, which is used to rapidly and robustly predict polarization-free incremental capacity analysis (ICA) curves in minutes, and physical modeling, which is used to quantitatively reveal the electrode-level degradation modes by decoupling them from the ICA curves. Only measured charging current and voltage signals are used. Results demonstrates that 11 points collected at any starting state-of-charge (SOC) in a minimum of 2.5 minutes are sufficient to obtain reliable ICA curves with a mean root mean square error (RMSE) of 0.2774 Ah/V. Accordingly, battery status can be accurately elevated based on their degradation at both macro and electrode levels. Through transfer learning, such a method can also be adapted to different battery chemistries, indicating the enticing potential for wide applications. |
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| ISSN: | 2667-1417 |