Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Hybrid Ensembles Allied with Data-Driven Approach

Shuai Zhao, Daming Sun, Yan Liu and Yuqi Liang ()
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Shuai Zhao: Intelligent Manufacturing Department, Shandong Labor Vocational and Technical College, Jinan 250300, China
Daming Sun: Intelligent Manufacturing Department, Shandong Labor Vocational and Technical College, Jinan 250300, China
Yan Liu: Intelligent Manufacturing Department, Shandong Labor Vocational and Technical College, Jinan 250300, China
Yuqi Liang: School of Mathematical Sciences, South China Normal University, Guangzhou 510631, China

Energies, 2025, vol. 18, issue 5, 1-16

Abstract: Capacity fade in lithium-ion batteries (LIBs) poses challenges for various industries. Predicting and preventing this fade is crucial, and hybrid methods for estimating remaining useful life (RUL) have become prevalent and achieved significant advancements. In this paper, we introduce a hybrid voting ensemble that combines Gradient Boosting, Random Forest, and K-Nearest Neighbors to forecast the fading capacity trend and knee point. We conducted extensive experiments using the CALCE CS2 datasets. The results indicate that our proposed approach outperforms single deep learning methods for RUL prediction and accurately identifies the knee point. Beyond prediction, this innovative method can potentially be integrated into real-world applications for broader use.

Keywords: lithium-ion battery; reaming useful life; ensemble learning; data-driven approach (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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