Lithium-ion batteries are essential in modern energy systems, supplying electricity to many consumer products that require dependable operation. This study introduces a novel method for predicting the remaining useful life (RUL) and end-of-life (EoL) of lithium-ion batteries (LIBs) using the long short-term memory (LSTM) model. The process includes getting the data ready, using exponential smoothing (ES), making a sequence, using the double lightning search algorithm (DLSA) to find the best hyperparameters, and doing iterative LSTM modeling. ES is utilized to preprocess data, reduce noise, and improve input quality for the subsequent model phases, while the iterative integration of LSTM modeling and DLSA hyperparameter optimization enhances capacity estimates across the entire battery lifecycle. The model performance is assessed using basic statistical measures, indicating consistent accuracy in predicting the state of health (SOH) and RUL. The way the time series is set up lets the LSTM accurately find important time-based relationships, even when there is much noise. Adaptive hyperparameter selection through DLSA safeguards against fluctuating degradation rates, yielding an average error of under ±0.002 ampere-hours in capacity prediction. Systematic sampling and normalization make calculations faster without changing the quality of the data. Data from the NASA Prognostics Center of Excellence on LIB performance and degradation are used to validate the model. This predictive modeling approach shows how data smoothing, deep learning, and intelligent search algorithms can improve RUL and EoL forecasts for real-time battery health monitoring and operational safety.
| [1] |
Y. Xiao, X. Shi, X. Li, et al., “Machine Learning Applied to Lithium-Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development,” Energy Storage6, no. 8 (2024): e70080, https://doi.org/10.1002/est2.70080.
|
| [2] |
M. K. Al-Alawi, J. Cugley, and H. Hassanin, “Techno-Economic Feasibility of Retired Electric-Vehicle Batteries Repurpose/Reuse in Second-Life Applications: A Systematic Review,” Energy and Climate Change3, no. November (2022): 100086, https://doi.org/10.1016/j.egycc.2022.100086.
|
| [3] |
J. W. Lee, M. H. S. M. Haram, G. Ramasamy, S. P. Thiagarajah, E. E. Ngu, and Y. H. Lee, “Technical Feasibility and Economics of Repurposed Electric Vehicles Batteries for Power Peak Shaving,” Journal of Energy Storage40, no. March (2021): 102752, https://doi.org/10.1016/j.est.2021.102752.
|
| [4] |
G. G. Njema, R. B. O. Ouma, and J. K. Kibet, “A Review on the Recent Advances in Battery Development and Energy Storage Technologies,” Journal of Renewable Energy2024 (2024): 1-35, https://doi.org/10.1155/2024/2329261.
|
| [5] |
A. Bisht, R. Amin, M. Dixit, N. Wood, C. B. M. Kweon, and I. Belharouak, “Impact of Cycling Conditions on Lithium-Ion Battery Performance for Electric Vertical Takeoff and Landing Applications,” Journal of Power Sources602 (2024): 234335, https://doi.org/10.1016/j.jpowsour.2024.234335.
|
| [6] |
H. Thomas, M. H. Weatherspoon, and R. Nelson, “Localized EIS Data for Capacity and SOH Prediction With Neural Networks,” Battery Energy. (2025): e70021, https://doi.org/10.1002/bte2.20250006.
|
| [7] |
B. He, H. Zheng, K. Tang, et al., “A Comprehensive Review of Lithium-Ion Battery (LiB) Recycling Technologies and Industrial Market Trend Insights,” Recycling9, no. 1 (2024): 9, https://doi.org/10.3390/recycling9010009.
|
| [8] |
A. Bhatt, W. Ongsakul, and M. N. Madhu, “Optimal Techno-Economic Feasibility Study of Net-Zero Carbon Emission Microgrid Integrating Second-Life Battery Energy Storage System,” Energy Conversion and Management266, no. February (2022): 115825, https://doi.org/10.1016/j.enconman.2022.115825.
|
| [9] |
Y. Miao, L. Liu, Y. Zhang, Q. Tan, and J. Li, “An Overview of Global Power Lithium-Ion Batteries and Associated Critical Metal Recycling,” Journal of Hazardous Materials425 (2022): 127900, https://doi.org/10.1016/j.jhazmat.2021.127900.
|
| [10] |
H. Iqbal, S. Sarwar, D. Kirli, J. K. H. Shek, and A. E. Kiprakis, “A Survey of Second-Life Batteries Based on Techno-Economic Perspective and Applications-Based Analysis,” Carbon Neutrality2, no. 1 (2023): 8, https://doi.org/10.1007/s43979-023-00049-5.
|
| [11] |
R. Guo, F. Wang, M. Akbar Rhamdhani, Y. Xu, and W. Shen, “Managing the Surge: A Comprehensive Review of the Entire Disposal Framework for Retired Lithium-Ion Batteries From Electric Vehicles,” Journal of Energy Chemistry92 (2024): 648-680, https://doi.org/10.1016/j.jechem.2024.01.055.
|
| [12] |
M. N. Akram and W. Abdul-Kader, “Repurposing Second-Life EV Batteries to Advance Sustainable Development: A Comprehensive Review,” Batteries10, no. 12 (2024): 452, https://doi.org/10.3390/batteries10120452.
|
| [13] |
S. Ansari, M. Ammirrul Atiqi Mohd Zainuri, A. Ayob, et al., “Expert Deep Learning Techniques for Remaining Useful Life Prediction of Diverse Energy Storage Systems: Recent Advances, Execution Features, Issues and Future Outlooks,” Expert Systems With Applications258, no. January (2024): 125163, https://doi.org/10.1016/j.eswa.2024.125163.
|
| [14] |
M. Waseem, M. Ahmad, A. Parveen, and M. Suhaib, “Battery Technologies and Functionality of Battery Management System for EVs: Current Status, Key Challenges, and Future Prospectives,” Journal of Power Sources580 (2023): 233349, https://doi.org/10.1016/j.jpowsour.2023.233349.
|
| [15] |
J. Thakur, C. Martins Leite de Almeida, and A. G. Baskar, “Electric Vehicle Batteries for a Circular Economy: Second Life Batteries as Residential Stationary Storage,” Journal of Cleaner Production375, no. August (2022): 134066, https://doi.org/10.1016/j.jclepro.2022.134066.
|
| [16] |
M. S. Reza, M. Mannan, M. Mansor, P. J. Ker, T. M. I. Mahlia, and M. A. Hannan, “Recent Advancement of Remaining Useful Life Prediction of Lithium-Ion Battery in Electric Vehicle Applications: A Review of Modelling Mechanisms, Network Configurations, Factors, and Outstanding Issues,” Energy Reports11 (2024): 4824-4848, https://doi.org/10.1016/j.egyr.2024.04.039.
|
| [17] |
X. Meng, W. Wang, X. Cao, G. Li, and D. Li, “A RUL Prediction Method of Lithium-Ion Battery Based on Extreme Learning Machine,” Journal of Physics: Conference Series2492, no. 1 (2023): 012026, https://doi.org/10.1088/1742-6596/2492/1/012026.
|
| [18] |
H. Kim, T. Jung, J. Jung, Y. Noh, and B. Lee, “Accurate Prediction of Electrochemical Degradation Trajectory for Lithium-Ion Battery Using Self-Discharge,” International Journal of Energy Research2024 (2024): 1758578, https://doi.org/10.1155/2024/1758578.
|
| [19] |
Z. Yu, N. Liu, Y. Zhang, L. Qi, and R. Li, “Battery SOH Prediction Based on Multi-Dimensional Health Indicators,” Batteries9, no. 2 (2023): 80, https://doi.org/10.3390/batteries9020080.
|
| [20] |
M. Ahwiadi and W. Wang, “Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies,” Batteries11, no. 1 (2025): 31, https://doi.org/10.3390/batteries11010031.
|
| [21] |
M. Liu, Z. Wen, and H. Su, “Deformation Prediction Based on Denoising Techniques and Ensemble Learning Algorithms for Concrete Dams,” Expert Systems With Applications238 (2024): 122022, https://doi.org/10.1016/j.eswa.2023.122022.
|
| [22] |
X. Gu, H. Bai, X. Cui, et al., “Challenges and Opportunities for Second-Life Batteries: Key Technologies and Economy,” Renewable and Sustainable Energy Reviews192 (2024): 114191, https://doi.org/10.1016/j.rser.2023.114191.
|
| [23] |
M. S. Hossain Lipu, M. S. A. Rahman, M. Mansor, et al., “Data Driven Health and Life Prognosis Management of Supercapacitor and Lithium-Ion Battery Storage Systems: Developments, Implementation Aspects, Limitations, and Future Directions,” Journal of Energy Storage98, no. PB (2024): 113172, https://doi.org/10.1016/j.est.2024.113172.
|
| [24] |
Y. Zhang, R. Xiong, H. He, and M. G. Pecht, “Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries,” IEEE Transactions on Vehicular Technology67, no. 7 (2018): 5695-5705, https://doi.org/10.1109/TVT.2018.2805189.
|
| [25] |
D. Chen, W. Hong, and X. Zhou, “Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries,” IEEE Access10 (2022): 19621-19628, https://doi.org/10.1109/ACCESS.2022.3151975.
|
| [26] |
C. Wang, N. Lu, S. Wang, Y. Cheng, and B. Jiang, “Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery,” Applied Sciences (Switzerland)8, no. 11 (2018): 2078, https://doi.org/10.3390/app8112078.
|
| [27] |
X. Li, D. Yu, V. Søren Byg, and S. Daniel Ioan, “The Development of Machine Learning-Based Remaining Useful Life Prediction for Lithium-Ion Batteries,” Journal of Energy Chemistry82 (2023): 103-121, https://doi.org/10.1016/j.jechem.2023.03.026.
|
| [28] |
P. Khumprom and N. Yodo, “A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries Based on a Deep Learning Algorithm,” Energies (Basel)12, no. 4 (2019): 660, https://doi.org/10.3390/en12040660.
|
| [29] |
M. S. Reza, M. A. Hannan, M. Mansor, P. J. Ker, S. K. Tiong, and M. J. Hossain, “Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty,” IEEE Transactions on Industry Applications60, no. 6 (2024): 9171-9183, https://doi.org/10.1109/TIA.2024.3429452.
|
| [30] |
Z. Lv, Y. Song, C. He, and L. Xu, “Remaining Useful Life Prediction for Lithium-Ion Batteries Incorporating Spatio-Temporal Information,” Journal of Energy Storage88, no. April (2024): 111626, https://doi.org/10.1016/j.est.2024.111626.
|
| [31] |
M. Yu, Y. Zhu, X. Gu, and Y. Shang, “Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Expansion Stress,” Journal of Electrical Engineering (China)19, no. 1 (2024): 49-56, https://doi.org/10.11985/2024.01.005.
|
| [32] |
X. Wang, K. Dai, M. Hu, and N. Ni, “Lithium-Ion Battery Health State and Remaining Useful Life Prediction Based on Hybrid Model MFE-GRU-TCA,” Journal of Energy Storage95, no. June (2024): 112442, https://doi.org/10.1016/j.est.2024.112442.
|
| [33] |
A. Jha, O. Dorkar, A. Biswas, and A. Emadi, “iTransformer Network Based Approach for Accurate Remaining Useful Life Prediction in Lithium-Ion Batteries,” 2024 IEEE Transportation Electrification Conference and Expo (ITEC), Chicago, IL, USA (2024): 1-8, https://doi.org/10.1109/ITEC60657.2024.10598898.
|
| [34] |
A. Tiane, C. Okar, M. Alzayed, and H. Chaoui, “Comparing Hybrid Approaches of Deep Learning for Remaining Useful Life Prognostic of Lithium-Ion Batteries,” IEEE Access12, no. April (2024): 70334-70344, https://doi.org/10.1109/ACCESS.2024.3394843.
|
| [35] |
Z. Wang, N. Liu, C. Chen, and Y. Guo, “Adaptive Self-Attention LSTM for RUL Prediction of Lithium-Ion Batteries,” Information Sciences635, no. January (2023): 398-413, https://doi.org/10.1016/j.ins.2023.01.100.
|
| [36] |
C. Li, X. Han, Q. Zhang, et al., “State-of-Health and Remaining-Useful-Life Estimations of Lithium-Ion Battery Based on Temporal Convolutional Network-Long Short-Term Memory,” Journal of Energy Storage74, no. PB (2023): 109498, https://doi.org/10.1016/j.est.2023.109498.
|
| [37] |
C. Chen, J. Wei, and Z. Li, “Remaining Useful Life Prediction for Lithium-Ion Batteries Based on a Hybrid Deep Learning Model,” Processes11, no. 8 (2023): 2333, https://doi.org/10.3390/pr11082333.
|
| [38] |
M. S. Reza, M. A. Hannan, M. Mansor, et al., “Towards Enhanced Remaining Useful Life Prediction of Lithium-Ion Batteries With Uncertainty Using Optimized Deep Learning Algorithm,” Journal of Energy Storage98, no. PA (2024): 113056, https://doi.org/10.1016/j.est.2024.113056.
|
| [39] |
Z. Mustaffa and M. H. Sulaiman, “Battery Remaining Useful Life Estimation Based on Particle Swarm Optimization-Neural Network,” Cleaner Energy Systems9 (2024): 100151, https://doi.org/10.1016/j.cles.2024.100151.
|
| [40] |
M. R. Fauzi, N. Yudistira, and W. F. Mahmudy, “State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth Embedding,” IEEE Access12 (2024): 14659-14670, https://doi.org/10.1109/ACCESS.2024.3357736.
|
| [41] |
X. Deng, E. Li, and H. Wang, “A Variable-Fidelity Multi-Objective Evolutionary Method for Polygonal Pin Fin Heat Sink Design,” Sustainability (Switzerland)15, no. 2 (2023): 1104, https://doi.org/10.3390/su15021104.
|
| [42] |
B. Lei, T. Q. Kirk, A. Bhattacharya, et al., “Bayesian Optimization With Adaptive Surrogate Models for Automated Experimental Design,” npj Computational Materials7, no. 1 (2021): 194, https://doi.org/10.1038/s41524-021-00662-x.
|
| [43] |
X. Qu, D. Shi, J. Zhao, et al., “Insights and Reviews on Battery Lifetime Prediction From Research to Practice,” Journal of Energy Chemistry94 (2024): 716-739, https://doi.org/10.1016/j.jechem.2024.03.013.
|
| [44] |
X. Hu, X. Deng, F. Wang, et al., “A Review of Second-Life Lithium-Ion Batteries for Stationary Energy Storage Applications,” Proceedings of the IEEE110, no. 6 (2022): 735-753, https://doi.org/10.1109/JPROC.2022.3175614.
|
RIGHTS & PERMISSIONS
2025 The Author(s). Battery Energy published by Xijing University and John Wiley & Sons Australia, Ltd.
PDF
