Lithium-ion battery remaining useful life prediction based on random forest machine learning
Accurately forecasting the Remaining Useful Life (RUL) of lithium-ion batteries is essential for maintaining reliability and maximizing the performance of battery powered systems. Traditional Random Forest Regression (RFR) techniques have demonstrated strong accuracy but often face computational cha...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2025
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| Online Access: | https://hdl.handle.net/10356/182343 |
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