LSTM-Based Battery Remaining Useful Life Prediction With Multi-Channel Charging Profiles
Remaining useful life (RUL) prediction of lithium-ion batteries can reduce the risk of battery failure by predicting the end of life. In this paper, we propose novel RUL prediction techniques based on long short-term memory (LSTM). To estimate RUL even in the presence of capacity regeneration phenom...
| Những tác giả chính: | , , , , |
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| Định dạng: | Bài viết |
| Ngôn ngữ: | English |
| Được phát hành: |
IEEE
2020-01-01
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| Loạt: | IEEE Access |
| Những chủ đề: | |
| Truy cập trực tuyến: | https://ieeexplore.ieee.org/document/8967059/ |