1
How to cite this paper
APA: Ajarmeh, A., Alieyan, K., Alkhasawneh, M., Alsmadi, I., Younes, M & Sawah, M. (2026). Hybrid deep learning approach for battery remaining useful life prediction using group-k-fold stacking. International Journal of Data and Network Science, 10(3), 1007-1018.
Chicago/Turabian: Ajarmeh, A., Alieyan, K., Alkhasawneh, M., Alsmadi, I., Younes, M & Sawah, M. 2026. "Hybrid deep learning approach for battery remaining useful life prediction using group-k-fold stacking." International Journal of Data and Network Science 10, no. 3 (2026): 1007-1018.
AMA: Ajarmeh, A., Alieyan, K., Alkhasawneh, M., Alsmadi, I., Younes, M & Sawah, M. Hybrid deep learning approach for battery remaining useful life prediction using group-k-fold stacking. International Journal of Data and Network Science. 2026;10(3):1007-1018.
References
Ahwiadi, M., & Wang, W. (2025). Battery health monitoring and remaining useful life prediction techniques: A review of technologies. Batteries, 11(1), 31.
Alam, S., Ayub, M. S., Arora, S., & Khan, M. A. (2023). An investigation of the imputation techniques for missing values in ordinal data enhancing clustering and classification analysis validity. Decision Analytics Journal, 9, 100341.
Al-Shalabi, M., Shehab, M., Alshammari, M. T., Alazmi, M., Alrawashdeh, R. O., Abualigah, L., & Mahdi, M. A. (2025). Optimal sizing of smart hybrid renewable energy system using Lotus Effect Optimization Algorithm. Energy Reports, 14, 1936-1948.
Ardeshiri, R. R., Liu, M., & Ma, C. (2022). Multivariate stacked bidirectional long short term memory for lithium-ion battery health management. Reliability Engineering & System Safety, 224, 108481.
Catelani, M., Ciani, L., Fantacci, R., Patrizi, G., & Picano, B. (2021). Remaining useful life estimation for prognostics of lithium-ion batteries based on recurrent neural network. IEEE Transactions on Instrumentation and Measurement, 70, 1-11.
Cavus, M., & Bell, M. (2025). Enabling smart grid resilience with deep learning-based battery health prediction in EV fleets. Batteries, 11(8), 283.
Chen, C., Wei, J., & Li, Z. (2023). Remaining useful life prediction for lithium-ion batteries based on a hybrid deep learning model. Processes, 11(8), 2333.
Chen, J. C., Chen, T. L., Liu, W. J., Cheng, C. C., & Li, M. G. (2021). Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery. Advanced Engineering Informatics, 50, 101405.
Chen, S., & Fang, X. (2025). Remaining useful life prediction of lithium–ion batteries via spatial attention TLSTM and dilated CNN with evolutionary optimization. Scientific Reports, 15(1), 32340.
Dai Nguyen, C., & Bae, S. J. (2023). Equivalent circuit simulated deep network architecture and transfer learning for remaining useful life prediction of lithium-ion batteries. Journal of energy storage, 71, 108042.
de la Iglesia, D. H., Corbacho, C. C., Dib, J. Z., Alonso-Secades, V., & López Rivero, A. J. (2025). Advanced machine learning and deep learning approaches for estimating the remaining life of EV batteries—A review. Batteries, 11(1), 17.
E Fakhry, A., Gomaa, M., A Metwaly, A., & Abdelhafeez, A. (2025). Machine Learning Models with Neutrosophic Numbers for Cloud Security Analysis and Smart Grid Control of Renewable Energy. Neutrosophic Sets and Systems, 85(1), 55.
Hafizhahullah, H., Yuliani, A. R., Pardede, H., Ramdan, A., Zilvan, V., Krisnandi, D., & Kadar, J. (2022, November). A hybrid cnn-lstm for battery remaining useful life prediction with charging profiles data. In Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications (pp. 106-110).
Hong, J., Lee, D., Jeong, E. R., & Yi, Y. (2020). Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning. Applied energy, 278, 115646.
Hsu, C. W., Xiong, R., Chen, N. Y., Li, J., & Tsou, N. T. (2022). Deep neural network battery life and voltage prediction by using data of one cycle only. Applied Energy, 306, 118134.
Iftikhar, M., Shoaib, M., Altaf, A., Iqbal, F., Villar, S. G., Dzul López, L. A., & Ashraf, I. (2024). A deep learning approach to optimize remaining useful life prediction for Li-ion batteries. Scientific Reports, 14(1), 25838.
Jafari, S., & Byun, Y. C. (2023). A CNN-GRU approach to the accurate prediction of batteries’ remaining useful life from charging profiles. Computers, 12(11), 219.
Jafari, S., Byun, Y. C., & Ko, S. (2023). A novel approach for predicting remaining useful life and capacity fade in lithium-ion batteries using hybrid machine learning. IEEE Access, 11, 131950-131963.
Jin, S., Sui, X., Huang, X., Wang, S., Teodorescu, R., & Stroe, D. I. (2021). Overview of machine learning methods for lithium-ion battery remaining useful lifetime prediction. Electronics, 10(24), 3126.
Jinlei, S., Lei, P., Ruihang, L., Qian, M., Chuanyu, T., & Tianru, W. (2019). Economic operation optimization for 2nd use batteries in battery energy storage systems. IEEE Access, 7, 41852-41859.
Khumprom, P., & Yodo, N. (2019). A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies, 12(4), 660.
Lee, C. J., Kim, B. K., Kwon, M. K., Nam, K., & Kang, S. W. (2021). Real-time prediction of capacity fade and remaining useful life of lithium-ion batteries based on charge/discharge characteristics. Electronics, 10(7), 846.
Li, P., Zhang, Z., Grosu, R., Deng, Z., Hou, J., Rong, Y., & Wu, R. (2022). An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries. Renewable and Sustainable Energy Reviews, 156, 111843.
Li, Y., Li, L., Mao, R., Zhang, Y., Xu, S., & Zhang, J. (2023). Hybrid data-driven approach for predicting the remaining useful life of lithium-ion batteries. IEEE Transactions on Transportation Electrification, 10(2), 2789-2805.
Lu, J., Xiong, R., Tian, J., Wang, C., Hsu, C. W., Tsou, N. T., ... & Li, J. (2022). Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning. Energy Storage Materials, 50, 139-151.
Ma, B., Yang, S., Zhang, L., Wang, W., Chen, S., Yang, X., ... & Liu, X. (2022). Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model. Journal of Power Sources, 548, 232030.
Musa, A. M., Abu-Shaikha, M. I., & Al-Abed, R. Y. (2025). Enhancing predictive accuracy of renewable energy systems and sustainable architectural design using PSO algorithm. International Journal of Computational and Experimental Science and Engineering, 11(1).
Nascimento, R. G., Corbetta, M., Kulkarni, C. S., & Viana, F. A. (2021). Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis. Journal of Power Sources, 513, 230526.
Park, K., Choi, Y., Choi, W. J., Ryu, H. Y., & Kim, H. (2020). LSTM-based battery remaining useful life prediction with multi-channel charging profiles. IEEE Access, 8, 20786-20798.
Qiao, J., Liu, X., & Chen, Z. (2020). Prediction of the remaining useful life of lithium-ion batteries based on empirical mode decomposition and deep neural networks. IEEE Access, 8, 42760-42767.
Rastegarpanah, A., Asif, M. E., & Stolkin, R. (2024). Hybrid neural networks for enhanced predictions of remaining useful life in lithium-ion batteries. Batteries, 10(3), 106.
Ren, L., Zhao, L., Hong, S., Zhao, S., Wang, H., & Zhang, L. (2018). Remaining useful life prediction for lithium-ion battery: A deep learning approach. IEEE Access, 6, 50587-50598.
Rouhi Ardeshiri, R., & Ma, C. (2021). Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach. International Journal of Energy Research, 45(11), 16633-16648.
Sawah, M. S., Elmannai, H., El-Bary, A. A., Lotfy, K., & Sheta, O. E. (2025). Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm. Scientific Reports, 15(1), 19063.
Shrinivasan, L., Taradale, A. S., & Verma, R. (2025, July). A Hybrid LSTM-RF model for Remaining Useful life prediction of Li-Ion Batteries. In 2025 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) (pp. 1-4). IEEE.
Tang, T., & Yuan, H. (2022). A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery. Reliability Engineering & System Safety, 217, 108082.
Toughzaoui, Y., Toosi, S. B., Chaoui, H., Louahlia, H., Petrone, R., Le Masson, S., & Gualous, H. (2022). State of health estimation and remaining useful life assessment of lithium-ion batteries: A comparative study. Journal of Energy Storage, 51, 104520.
Tran, K., Le, T., Huynh, B., Trinh, H. C., Nguyen, V. R., Nguyen-Thoi, T., & Nguyen-Thai, V. (2026). HyBattNet: Hybrid Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries. IEEE Access.
Wang, J., Zhang, S., Li, C., Wu, L., & Wang, Y. (2022). A data-driven method with mode decomposition mechanism for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Power Electronics, 37(11), 13684-13695.
Wang, Y., Hei, C., Liu, H., Zhang, S., & Wang, J. (2022). Prognostics of remaining useful life for lithium-ion batteries based on hybrid approach of linear pattern extraction and nonlinear relationship mining. IEEE Transactions on Power Electronics, 38(1), 1054-1063.
Wu, T., Cheng, K., Kang, J., & Liu, R. (2024). Remaining Useful Life Prediction of Lithium‐Ion Batteries Based on a Combination of Ensemble Empirical Mode Decomposition and Deep Belief Network–Long Short‐Term Memory. Energy Technology, 12(5), 2301033.
Xing, Y., Ma, E. W., Tsui, K. L., & Pecht, M. (2011). Battery management systems in electric and hybrid vehicles. Energies, 4(11), 1840-1857.
Xu, Q., Wu, M., Khoo, E., Chen, Z., & Li, X. (2023). A hybrid ensemble deep learning approach for early prediction of battery remaining useful life. IEEE/CAA Journal of Automatica Sinica, 10(1), 177-187.
Xu, W., Mao, R., Han, P., Yuan, N., Li, Y., Guo, Y., & Zhang, H. (2025). A comprehensive review of lithium-ion battery remaining useful life prediction: methodologies, datasets, performance metrics, and future perspectives. Measurement Science and Technology, 36(8), 082001.
Yan, L., Peng, J., Gao, D., Wu, Y., Liu, Y., Li, H., ... & Huang, Z. (2022). A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery. Energy, 243, 123038.
Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018). Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology, 67(7), 5695-5705.
Zhao, S., Sun, D., Liu, Y., & Liang, Y. (2025). Remaining useful life prediction for lithium-ion batteries based on hybrid ensembles allied with data-driven approach. Energies, 18(5), 1114.
Zraibi, B., Okar, C., Chaoui, H., & Mansouri, M. (2021). Remaining useful life assessment for lithium-ion batteries using CNN-LSTM-DNN hybrid method. IEEE Transactions on Vehicular Technology, 70(5), 4252-4261.
Alam, S., Ayub, M. S., Arora, S., & Khan, M. A. (2023). An investigation of the imputation techniques for missing values in ordinal data enhancing clustering and classification analysis validity. Decision Analytics Journal, 9, 100341.
Al-Shalabi, M., Shehab, M., Alshammari, M. T., Alazmi, M., Alrawashdeh, R. O., Abualigah, L., & Mahdi, M. A. (2025). Optimal sizing of smart hybrid renewable energy system using Lotus Effect Optimization Algorithm. Energy Reports, 14, 1936-1948.
Ardeshiri, R. R., Liu, M., & Ma, C. (2022). Multivariate stacked bidirectional long short term memory for lithium-ion battery health management. Reliability Engineering & System Safety, 224, 108481.
Catelani, M., Ciani, L., Fantacci, R., Patrizi, G., & Picano, B. (2021). Remaining useful life estimation for prognostics of lithium-ion batteries based on recurrent neural network. IEEE Transactions on Instrumentation and Measurement, 70, 1-11.
Cavus, M., & Bell, M. (2025). Enabling smart grid resilience with deep learning-based battery health prediction in EV fleets. Batteries, 11(8), 283.
Chen, C., Wei, J., & Li, Z. (2023). Remaining useful life prediction for lithium-ion batteries based on a hybrid deep learning model. Processes, 11(8), 2333.
Chen, J. C., Chen, T. L., Liu, W. J., Cheng, C. C., & Li, M. G. (2021). Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery. Advanced Engineering Informatics, 50, 101405.
Chen, S., & Fang, X. (2025). Remaining useful life prediction of lithium–ion batteries via spatial attention TLSTM and dilated CNN with evolutionary optimization. Scientific Reports, 15(1), 32340.
Dai Nguyen, C., & Bae, S. J. (2023). Equivalent circuit simulated deep network architecture and transfer learning for remaining useful life prediction of lithium-ion batteries. Journal of energy storage, 71, 108042.
de la Iglesia, D. H., Corbacho, C. C., Dib, J. Z., Alonso-Secades, V., & López Rivero, A. J. (2025). Advanced machine learning and deep learning approaches for estimating the remaining life of EV batteries—A review. Batteries, 11(1), 17.
E Fakhry, A., Gomaa, M., A Metwaly, A., & Abdelhafeez, A. (2025). Machine Learning Models with Neutrosophic Numbers for Cloud Security Analysis and Smart Grid Control of Renewable Energy. Neutrosophic Sets and Systems, 85(1), 55.
Hafizhahullah, H., Yuliani, A. R., Pardede, H., Ramdan, A., Zilvan, V., Krisnandi, D., & Kadar, J. (2022, November). A hybrid cnn-lstm for battery remaining useful life prediction with charging profiles data. In Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications (pp. 106-110).
Hong, J., Lee, D., Jeong, E. R., & Yi, Y. (2020). Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning. Applied energy, 278, 115646.
Hsu, C. W., Xiong, R., Chen, N. Y., Li, J., & Tsou, N. T. (2022). Deep neural network battery life and voltage prediction by using data of one cycle only. Applied Energy, 306, 118134.
Iftikhar, M., Shoaib, M., Altaf, A., Iqbal, F., Villar, S. G., Dzul López, L. A., & Ashraf, I. (2024). A deep learning approach to optimize remaining useful life prediction for Li-ion batteries. Scientific Reports, 14(1), 25838.
Jafari, S., & Byun, Y. C. (2023). A CNN-GRU approach to the accurate prediction of batteries’ remaining useful life from charging profiles. Computers, 12(11), 219.
Jafari, S., Byun, Y. C., & Ko, S. (2023). A novel approach for predicting remaining useful life and capacity fade in lithium-ion batteries using hybrid machine learning. IEEE Access, 11, 131950-131963.
Jin, S., Sui, X., Huang, X., Wang, S., Teodorescu, R., & Stroe, D. I. (2021). Overview of machine learning methods for lithium-ion battery remaining useful lifetime prediction. Electronics, 10(24), 3126.
Jinlei, S., Lei, P., Ruihang, L., Qian, M., Chuanyu, T., & Tianru, W. (2019). Economic operation optimization for 2nd use batteries in battery energy storage systems. IEEE Access, 7, 41852-41859.
Khumprom, P., & Yodo, N. (2019). A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies, 12(4), 660.
Lee, C. J., Kim, B. K., Kwon, M. K., Nam, K., & Kang, S. W. (2021). Real-time prediction of capacity fade and remaining useful life of lithium-ion batteries based on charge/discharge characteristics. Electronics, 10(7), 846.
Li, P., Zhang, Z., Grosu, R., Deng, Z., Hou, J., Rong, Y., & Wu, R. (2022). An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries. Renewable and Sustainable Energy Reviews, 156, 111843.
Li, Y., Li, L., Mao, R., Zhang, Y., Xu, S., & Zhang, J. (2023). Hybrid data-driven approach for predicting the remaining useful life of lithium-ion batteries. IEEE Transactions on Transportation Electrification, 10(2), 2789-2805.
Lu, J., Xiong, R., Tian, J., Wang, C., Hsu, C. W., Tsou, N. T., ... & Li, J. (2022). Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning. Energy Storage Materials, 50, 139-151.
Ma, B., Yang, S., Zhang, L., Wang, W., Chen, S., Yang, X., ... & Liu, X. (2022). Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model. Journal of Power Sources, 548, 232030.
Musa, A. M., Abu-Shaikha, M. I., & Al-Abed, R. Y. (2025). Enhancing predictive accuracy of renewable energy systems and sustainable architectural design using PSO algorithm. International Journal of Computational and Experimental Science and Engineering, 11(1).
Nascimento, R. G., Corbetta, M., Kulkarni, C. S., & Viana, F. A. (2021). Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis. Journal of Power Sources, 513, 230526.
Park, K., Choi, Y., Choi, W. J., Ryu, H. Y., & Kim, H. (2020). LSTM-based battery remaining useful life prediction with multi-channel charging profiles. IEEE Access, 8, 20786-20798.
Qiao, J., Liu, X., & Chen, Z. (2020). Prediction of the remaining useful life of lithium-ion batteries based on empirical mode decomposition and deep neural networks. IEEE Access, 8, 42760-42767.
Rastegarpanah, A., Asif, M. E., & Stolkin, R. (2024). Hybrid neural networks for enhanced predictions of remaining useful life in lithium-ion batteries. Batteries, 10(3), 106.
Ren, L., Zhao, L., Hong, S., Zhao, S., Wang, H., & Zhang, L. (2018). Remaining useful life prediction for lithium-ion battery: A deep learning approach. IEEE Access, 6, 50587-50598.
Rouhi Ardeshiri, R., & Ma, C. (2021). Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach. International Journal of Energy Research, 45(11), 16633-16648.
Sawah, M. S., Elmannai, H., El-Bary, A. A., Lotfy, K., & Sheta, O. E. (2025). Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm. Scientific Reports, 15(1), 19063.
Shrinivasan, L., Taradale, A. S., & Verma, R. (2025, July). A Hybrid LSTM-RF model for Remaining Useful life prediction of Li-Ion Batteries. In 2025 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) (pp. 1-4). IEEE.
Tang, T., & Yuan, H. (2022). A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery. Reliability Engineering & System Safety, 217, 108082.
Toughzaoui, Y., Toosi, S. B., Chaoui, H., Louahlia, H., Petrone, R., Le Masson, S., & Gualous, H. (2022). State of health estimation and remaining useful life assessment of lithium-ion batteries: A comparative study. Journal of Energy Storage, 51, 104520.
Tran, K., Le, T., Huynh, B., Trinh, H. C., Nguyen, V. R., Nguyen-Thoi, T., & Nguyen-Thai, V. (2026). HyBattNet: Hybrid Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries. IEEE Access.
Wang, J., Zhang, S., Li, C., Wu, L., & Wang, Y. (2022). A data-driven method with mode decomposition mechanism for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Power Electronics, 37(11), 13684-13695.
Wang, Y., Hei, C., Liu, H., Zhang, S., & Wang, J. (2022). Prognostics of remaining useful life for lithium-ion batteries based on hybrid approach of linear pattern extraction and nonlinear relationship mining. IEEE Transactions on Power Electronics, 38(1), 1054-1063.
Wu, T., Cheng, K., Kang, J., & Liu, R. (2024). Remaining Useful Life Prediction of Lithium‐Ion Batteries Based on a Combination of Ensemble Empirical Mode Decomposition and Deep Belief Network–Long Short‐Term Memory. Energy Technology, 12(5), 2301033.
Xing, Y., Ma, E. W., Tsui, K. L., & Pecht, M. (2011). Battery management systems in electric and hybrid vehicles. Energies, 4(11), 1840-1857.
Xu, Q., Wu, M., Khoo, E., Chen, Z., & Li, X. (2023). A hybrid ensemble deep learning approach for early prediction of battery remaining useful life. IEEE/CAA Journal of Automatica Sinica, 10(1), 177-187.
Xu, W., Mao, R., Han, P., Yuan, N., Li, Y., Guo, Y., & Zhang, H. (2025). A comprehensive review of lithium-ion battery remaining useful life prediction: methodologies, datasets, performance metrics, and future perspectives. Measurement Science and Technology, 36(8), 082001.
Yan, L., Peng, J., Gao, D., Wu, Y., Liu, Y., Li, H., ... & Huang, Z. (2022). A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery. Energy, 243, 123038.
Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018). Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology, 67(7), 5695-5705.
Zhao, S., Sun, D., Liu, Y., & Liang, Y. (2025). Remaining useful life prediction for lithium-ion batteries based on hybrid ensembles allied with data-driven approach. Energies, 18(5), 1114.
Zraibi, B., Okar, C., Chaoui, H., & Mansouri, M. (2021). Remaining useful life assessment for lithium-ion batteries using CNN-LSTM-DNN hybrid method. IEEE Transactions on Vehicular Technology, 70(5), 4252-4261.