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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Nanyang Technological University
2025
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sg-ntu-dr.10356-1823432025-01-24T15:48:12Z Lithium-ion battery remaining useful life prediction based on random forest machine learning Li, Xinwei Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering Lithium-ion battery Remaining useful life Random forest 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 challenges as data dimensionality grows. Random Projection Forests (RPF) offer a promising solution by integrating random projections into the ensemble framework. They retain the predictive capabilities of RFR while significantly reducing computational overhead. This dissertation investigates the application of RPF and RFR algorithms to lithiumion battery RUL prediction, evaluating their accuracy, scalability, and adaptability to complex, high-dimensional data. The findings show that RPF achieves predictive performance comparable to RFR, yet with improved computational efficiency, thereby providing practical guidance for implementing more effective and resource-efficient battery management strategies. Master's degree 2025-01-23T03:33:44Z 2025-01-23T03:33:44Z 2025 Thesis-Master by Coursework Li, X. (2025). Lithium-ion battery remaining useful life prediction based on random forest machine learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/182343 https://hdl.handle.net/10356/182343 en D-257-23241-06521 application/pdf Nanyang Technological University |
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Engineering Lithium-ion battery Remaining useful life Random forest |
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Engineering Lithium-ion battery Remaining useful life Random forest Li, Xinwei Lithium-ion battery remaining useful life prediction based on random forest machine learning |
| description |
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 challenges as data dimensionality grows. Random Projection Forests (RPF) offer a promising solution by integrating random projections into the ensemble framework. They retain the predictive capabilities of RFR while significantly reducing computational overhead. This dissertation investigates the application of RPF and RFR algorithms to lithiumion battery RUL prediction, evaluating their accuracy, scalability, and adaptability to complex, high-dimensional data. The findings show that RPF achieves predictive performance comparable to RFR, yet with improved computational efficiency, thereby providing practical guidance for implementing more effective and resource-efficient battery management strategies. |
| author2 |
Xu Yan |
| author_facet |
Xu Yan Li, Xinwei |
| format |
Thesis-Master by Coursework |
| author |
Li, Xinwei |
| author_sort |
Li, Xinwei |
| title |
Lithium-ion battery remaining useful life prediction based on random forest machine learning |
| title_short |
Lithium-ion battery remaining useful life prediction based on random forest machine learning |
| title_full |
Lithium-ion battery remaining useful life prediction based on random forest machine learning |
| title_fullStr |
Lithium-ion battery remaining useful life prediction based on random forest machine learning |
| title_full_unstemmed |
Lithium-ion battery remaining useful life prediction based on random forest machine learning |
| title_sort |
lithium-ion battery remaining useful life prediction based on random forest machine learning |
| publisher |
Nanyang Technological University |
| publishDate |
2025 |
| url |
https://hdl.handle.net/10356/182343 |
| _version_ |
1823108702104715264 |