Lithium-ion battery remaining useful life prediction based on random forest machine learning

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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書目詳細資料
主要作者:	Li, Xinwei
其他作者:	Xu Yan
格式:	Thesis-Master by Coursework
語言:	English
出版:	Nanyang Technological University 2025
主題:	Engineering Lithium-ion battery Remaining useful life Random forest
在線閱讀:	https://hdl.handle.net/10356/182343
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

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