Application of state of health estimation and remaining useful life prediction for lithium-ion batteries based on AT-CNN-BiLSTM
Abstract Ensuring the long-term safe usage of lithium-ion batteries hinges on accurately estimating the State of Health $$(\textrm{SOH})$$ and predicting the Remaining Useful Life (RUL). This study proposes a novel prediction method based on a AT-CNN-BiLSTM architecture. Initially, key parameters su...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-80421-2 |
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