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

• Main Author: Li, Xinwei
• Other Authors: Xu Yan
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2025
• Subjects: Engineering; Lithium-ion battery; Remaining useful life; Random forest
• Online Access: https://hdl.handle.net/10356/182343
• Institution: Nanyang Technological University

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...