REFERENCES
1. Sun, H.; Jiang, H.; Gu, Z.; et al. A novel multiple kernel extreme learning machine model for remaining useful life prediction of lithium-ion batteries. J. Power. Sources. 2024, 613, 234912.
2. Wang, S.; Li, Y. T.; Chen, L. F.; Su, X. H.; Zhou, S. B. Remaining useful life prediction of lithium-ion batteries based on IMM-PFF. Acta. Electron. Sin. 2025, 53, 1520-32.
3. Madani, S. S.; Shabeer, Y.; Fowler, M.; et al. Artificial intelligence and digital twin technologies for intelligent lithium-ion battery management systems. Batteries 2025, 11, 298.
4. Jia, D.; Ren, Y.; Li, L.; Yun, P.; Xue, Y.; Mi, Y. Research on optimization of hybrid energy storage capacity using ensemble empirical mode ecomposition and fuzzy control. Acta. Energ. Sol. Sin. 2023, 44, 239-46.
5. Zhu, Z. W.; Miao, J. W.; Zhu, X. Y.; et al. Research progress in remaining useful life prediction of lithium-ion batteries based on machine learning. Energy. Storage. Sci. Technol. 2024, 13, 3134-49.
6. Li, Y.; Wang, J. F.; Mo, W. Q.; Zhang, X. L. A review of remaining useful life prediction for lithium-ion batteries based on data-driven method. J. Power. Supply. 2025, 23, 253-65.
7. Ahwiadi, M.; Wang, W. A smart evolving fuzzy predictor with customized firefly optimization for battery RUL prediction. Batteries 2025, 11, 362.
8. Demirci, O.; Taskin, S.; Schaltz, E.; Acar Demirci, B. Review of battery state estimation methods for electric vehicles-Part II: SOH estimation. J. Energy. Storage. 2024, 96, 112703.
9. Liu, Y.; Tian, Y.; Liu, D.; et al. A novel dual gated recurrent unit neural network based on error compensation integrated with Kalman filter for the state of charge estimation of parallel battery modules. J. Power. Sources. 2025, 635, 236508.
10. Liu, B.; Yin, J.; Li, R. Improved particle filter algorithm for remaining useful life prediction of lithium-ion batteries. Power. Syst. Prot. Control. 2024, 52, 124-31.
11. Qiang, H.; Zhang, W.; Ding, K. A prediction framework for state of health of lithium-ion batteries based on improved support vector regression. J. Electrochem. Soc. 2023, 170, 110517.
12. Qaadan, S.; Alshare, A.; Popp, A.; Schmuelling, B. Prediction of lithium-ion battery health using GRU-BPP. Batteries 2024, 10, 399.
13. Zhao, S.; Sun, D.; Liu, Y.; Liang, Y. Remaining useful life prediction for lithium-ion batteries based on hybrid ensembles allied with data-driven approach. Energies 2025, 18, 1114.
14. Qian, K.; Li, Y.; Zou, Q.; Cao, K.; Li, Z. SOH and RUL estimation for lithium-ion batteries based on partial charging curve features. Energies 2025, 18, 3248.
15. Wang, X. M.; He, Y.; Wang, L. L.; Wu, H. B.; Xu, B.; Zhao, W. G. RUL prediction for lithium-ion batteries based on DWD-SVR model. Acta. Energ. Sol. Sin. 2025, 46, 52-9.
16. Cai, Y.; Wang, X.; Jiang, K.; Han, D.; Zhao, Q. Multi-step prediction of online lithium battery remaining useful life based on GRNN-GSA-ELM. J. Mech. Eng. 2024, 60, 296-308.
17. Zhang, T.; Sun, H. A method for predicting the remaining life of lithium-ion batteries based on an improved Dempster–Shafer evidence theory framework. Energies 2025, 18, 3370.
18. Sun, J.; Zhai, Q. C. A novel remaining useful life prediction method based on fusion feature and OOA-BiGRU for lithium-ion batteries. Trans. China. Electrotech. Soc. 2025, 40, 2996-3012.
19. Lv, K.; Ma, Z.; Bao, C.; Liu, G. Indirect prediction of lithium-ion battery RUL based on CEEMDAN and CNN-BiGRU. Energies 2024, 17, 1704.
20. Niu, W.; Li, X.; Tian, H.; Liang, C. Remaining useful life prediction of PEMFC based on 2-layer bidirectional LSTM network. World. Electr. Veh. J. 2025, 16, 511.
21. Liu, B.; Ji, C. L.; Cao, L. J.; Wu, X. Y.; Duan, Y. F. Prediction of remaining service life of lithium-ion batteries based on complete ensemble empirical mode decomposition with adaptive noise and BiLSTM-Transformer. Power. Syst. Prot. Control. 2024, 52, 167-77.
22. Mou, J.; Yang, Q.; Tang, Y.; Liu, Y.; Li, J.; Yu, C. Prediction of the remaining useful life of lithium-ion batteries based on the 1D CNN-BLSTM neural network. Batteries 2024, 10, 152.
23. Wang, X.; Zhang, J. Lithium battery lifespan prediction method integrating dynamic convolution transformer and CMA-ES. Acta. Energ. Sol. Sin. 2025, 46, 1-8.
24. Sun, Y.; An, J.; Huang, C.; et al. Battery life evaluation method based on temporal convolution network. Adv. Eng. Sci. 2025, 57, 259-68.
25. Yin, J.; Liu, B.; Sun, G.; Qian, X. Transfer learning DAE-LSTM for remaining useful life prediction of Li-ion batteries. Trans. China. Electrotech. Soc. 2024, 39, 290-302.
26. Saleem, U.; Liu, W.; Riaz, S.; et al. TransRUL: a transformer-based multihead attention model for enhanced prediction of battery remaining useful life. Energies 2024, 17, 3976.
27. Capoglu, E. U.; Taherkhani, A. A comparison of different transformer models for time series prediction. Information 2025, 16, 878.
28. Zhu, X.; Li, L.; Wang, G.; Shi, N.; Li, Y.; Yang, X. A lithium-ion battery remaining useful life prediction method based on mode decomposition and informer-LSTM. Electronics 2025, 14, 3886.
29. Wang, Z.; Ma, Y.; Gao, J.; Chen, H. Remaining useful life prediction for solid-state lithium batteries based on spatial–temporal relations and neuronal ODE-assisted KAN. Reliab. Eng. Syst. Saf. 2025, 260, 111003.
30. Jiang, L.; Li, Z.; Hu, C.; Chen, J.; Huang, Q.; He, G. A robust adapted flexible parallel neural network architecture for early prediction of lithium battery lifespan. Energy 2024, 308, 132840.
31. Zhou, J.; Huang, W.; Li, Y.; Wang, Z. Life prediction of lithium battery based on particle filter and BP neural network. J. Phys. Conf. Ser. 2024, 2814, 012047.
32. Huang, J.; Liu, L.; Zhao, H.; Li, T.; Li, B. RUL-Mamba: Mamba-based remaining useful life prediction for lithium-ion batteries. J. Energy. Storage. 2025, 120, 116376.
33. Liu, C.; Min, W.; Song, J.; et al. Channel grouping vision transformer for lightweight fruit and vegetable recognition. Expert. Syst. Appl. 2025, 292, 128636.
34. Liang, Y.; Zhao, S. Early prediction of remaining useful life for lithium-ion batteries with the state space model. Energies 2024, 17, 6326.
35. Liao, Z.; Lv, D.; Hu, Q.; Zhang, X. Review on aging risk assessment and life prediction technology of lithium energy storage batteries. Energies 2024, 17, 3668.
