Hybrid XGBoost–LSTM Model for State of Health and Remaining Useful Life Prediction of Lithium-Ion Batteries

Kaur, Amarjot; Chauhan, Shristi

doi:https://doi.org/10.55041/ijcope.v2i5.018

Volume 02, Issue 05

Published on: May 2026

HYBRID XGBOOST–LSTM MODEL FOR STATE OF HEALTH AND REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES

Amarjot Kaur Shristi Chauhan

Dr. R. K. Pongiannan

Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India-603203

DOI:https://doi.org/10.55041/ijcope.v2i5.018

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Abstract

For electric cars and energy storage devices to be more dependable, secure, and simple to maintain, it’s crucial to possess the ability to precisely forecast the State of Health (SoH) and Lithium-ion battery Remaining Useful Life (RUL). Typical Structured battery data is a good fit for machine learning models. However, they don’t always recognize how things evolve over time. Models for deep learning, like Long Short Term Memory (LSTM) networks, learn sequential patterns well, but they need a lot of data and processing power. This study shows a hybrid prediction system which combines XGBoost and LSTM models to use feature-based learning and temporal dependency modeling to guess the battery life. SoH. Experimental evaluations are performed using publicly available NASA lithium-ion battery discharge datasets. We use the Leave-One-Battery-Out strategy to measure how well the model works using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results show that the proposed hybrid model works better and more consistently than individual models on a number of battery datasets. The predicted SoH also makes it possible to use data to guess RUL, which is useful for real-world battery health monitoring.

Keywords— Lithium-ion battery; State of Health (SoH); Remaining Useful Life (RUL); XGBoost; Long Short-Term Mem ory (LSTM); Hybrid machine learning model; Battery health prediction

How to Cite this Paper

Kaur, A. & Chauhan, S. (2026). Hybrid XGBoost–LSTM Model for State of Health and Remaining Useful Life Prediction of Lithium-Ion Batteries. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.018

Kaur, Amarjot, and Shristi Chauhan. "Hybrid XGBoost–LSTM Model for State of Health and Remaining Useful Life Prediction of Lithium-Ion Batteries." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.018.

Kaur, Amarjot, and Shristi Chauhan. "Hybrid XGBoost–LSTM Model for State of Health and Remaining Useful Life Prediction of Lithium-Ion Batteries." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.018.

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References

A. Hasib, S. Islam, R. K. Chakrabortty, M. J. Ryan, D. K. Saha, M. H. Ahamed, S. I. Moyeen, S. K. Das, M. F. Ali, M. R. Islam, et al., “A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management,” IEEE Trans., vol. 9, pp. 86166–86193, 2021.

Roman, S. Saxena, V. Robu, M. Pecht, and D. Flynn, “Machine learning pipeline for battery state-of-health estimation,” Nature Machine Intelligence, vol. 3, no. 5, pp. 447–456, Apr. 2021, doi: 10.1038/s42256-021-00312-3.

Wu, X. Cui, H. Zhang, and M. Lin, “Health Prognosis With Optimized Feature Selection for Lithium-Ion Battery in Electric Vehicle Applications,” IEEE Trans. Power Electron., vol. 36, pp. 12646–12655, 2021.

Mamo and F.-K. Wang, “Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries,” Batteries, vol. 7, no. 4, p. 66, 2021, doi: 10.3390/batteries7040066.

Lu, R. Xiong, J. Tian, C. Wang, and F. Sun, “Deep learning to estimate lithium-ion battery state of health without additional degradation experiments,” Nature Communications, vol. 14, no. 1, p. 2760, May 2023, doi: 10.1038/s41467-023-38458-w.

Ding, W. Wang, and T. Zhu, “Remaining useful life prediction for lithium-ion batteries based on CS-VMD and GRU,” IEEE Access, vol.10, pp. 89402–89413, 2022.

Durmus and S. Karagol, “Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP,”Applied Sciences, vol. 14, no. 13, p. 5662, 2024, doi: 10.3390/app14135662.

Zhang, S. Zhao, and Y. He, “An integrated method of the future capacity and RUL prediction for lithium-ion battery pack,” IEEE Trans. Veh. Technol., vol. 71, pp. 2601–2613, 2021.

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•Published on: May 03 2026

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