What question did this study set out to answer?

This research aims to enhance the accuracy of remaining useful life prediction for lithium-ion batteries using a novel deep learning framework.

June 12, 2026International Journal of Data and Network Science1 citationsOpen Access

Hybrid deep learning approach for battery remaining useful life prediction using group-k-fold stacking

AAAhmad Ajarmeh KAKamal Alieyan MAMutasem Sh Alkhasawneh

Key Points

This research aims to enhance the accuracy of remaining useful life prediction for lithium-ion batteries using a novel deep learning framework.
Proposed a leakage-safe learning framework for RUL prediction using cycling features.
Implemented an out-of-fold stacking ensemble with GroupKFold to avoid information leakage.
Conducted experiments on a public Battery RUL dataset and evaluated performance using various regression metrics.
Achieved R² = 0.999550, MAE = 5.297907, RMSE = 6.834146, sMAPE = 3.080569, WAPE = 0.958618.
Proposed stacking approach outperformed baseline models including XGBoost and Random Forest.
Confirmed that leakage-safe group-aware stacking significantly enhances prediction accuracy and stability.

Abstract

Accurate Remaining Useful Life (RUL) prediction is essential for reliable battery management and cost-effective maintenance in lithium-ion energy storage applications. This study proposes a leakage-safe learning framework for battery RUL prediction from numerical cycling features, emphasizing realistic generalization to unseen batteries through group-aware splitting. The core contribution is a proper out-of-fold (OOF) stacking ensemble trained under GroupKFold to prevent information leakage across samples from the same battery while enabling an effective meta-learner to combine diverse learners. Experiments are conducted on a public Battery RUL dataset from Kaggle, and performance is evaluated using complementary regression metrics (R², MAE, RMSE) and robust percentage errors (sMAPE, WAPE). Results show that the proposed stacking approach (OOF GroupKFold with Meta-XGBoost) achieves the best overall performance (R² = 0.999550, MAE = 5.297907, RMSE = 6.834146, sMAPE = 3.080569, WAPE = 0.958618), outperforming strong baselines including XGBoost and Random Forest. These findings confirm that leakage-safe group-aware stacking can significantly enhance accuracy and stability for battery RUL prediction in practical deployment settings.

问 AI

Bookmark

View Full Paper