REFERENCES

1. Zhao, S.; Guo, Z.; Yan, K.; et al. Towards high-energy-density lithium-ion batteries: strategies for developing high-capacity lithium-rich cathode materials. Energy. Storage. Mater. 2021, 34, 716-34.

2. Elmahallawy, M.; Elfouly, T.; Alouani, A.; Massoud, A. M. A comprehensive review of lithium-ion batteries modeling, and state of health and remaining useful lifetime prediction. IEEE. Access. 2022, 10, 119040-70.

3. Zhao, J.; Burke, A. F. Electric vehicle batteries: status and perspectives of data-driven diagnosis and prognosis. Batteries 2022, 8, 142.

4. Wu, X.; Lv, S.; Li, J.; et al. Enhanced energy density and fast-charging ability via directional particle configuration. J. Energy. Chem. 2024, 90, 152-64.

5. Sonika; Singh, N. Hybrid MXenes for supercapacitors: advances, mechanisms, and outlook. Mater. Today. Phys. 2025, 59, 101947.

6. Zhang, B.; Liu, W.; Tian, Y.; et al. Optimization strategies for high performance zinc-ion hybrid supercapacitors: advances in materials, interfaces and devices. Energy. Storage. Mater. 2025, 80, 104449.

7. Sahani, S.; Mahajan, H.; Han, S. S. Unveiling the hybrid era: advancement in electrode materials for the high-performance supercapacitor: a comprehensive review. J. Energy. Storage. 2024, 90, 111808.

8. Su, L.; Xu, Y.; Dong, Z. State‐of‐health estimation of lithium‐ion batteries: a comprehensive literature review from cell to pack levels. Energy. Convers. Econ. 2024, 5, 224-42.

9. Su, L.; Wu, M.; Li, Z.; Zhang, J. Cycle life prediction of lithium-ion batteries based on data-driven methods. eTransportation 2021, 10, 100137.

10. Hu, X.; Xu, L.; Lin, X.; Pecht, M. Battery lifetime prognostics. Joule 2020, 4, 310-46.

11. Zhao, J.; Zhu, Y.; Zhang, B.; et al. Review of state estimation and remaining useful life prediction methods for lithium-ion batteries. Sustainability 2023, 15, 5014.

12. Sun, Y.; Xiong, R.; Wang, P.; Li, H.; Sun, F. A deep learning approach for enhanced degradation diagnostics of NMC lithium-ion batteries via impedance spectra. J. Energy. Chem. 2025, 107, 894-907.

13. Lin, C.; Wu, L.; Tuo, X.; et al. A lightweight two-stage physics-informed neural network for SOH estimation of lithium-ion batteries with different chemistries. J. Energy. Chem. 2025, 105, 261-79.

14. Li, Y.; Li, L.; Li, L.; et al. Research on hybrid data-driven method for predicting the remaining useful life of lithium-ion batteries. Comput. Phys. Commun. 2025, 309, 109500.

15. Wei, Z.; Wu, M.; Wu, J.; et al. Localized feature selection augmented dual-stream fusion network for state of health estimation of lithium-ion batteries. J. Energy. Chem. 2025, 109, 879-92.

16. Wang, F.; Zhai, Z.; Zhao, Z.; Di, Y.; Chen, X. Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nat. Commun. 2024, 15, 4332.

17. Wu, J.; Sun, Z.; Li, D.; et al. Efficient estimating and clustering lithium-ion batteries with a deep-learning approach. Commun. Eng. 2025, 4, 151.

18. Samanta, A.; Chowdhuri, S.; Williamson, S. S. Machine learning-based data-driven fault detection/diagnosis of lithium-ion battery: a critical review. Electronics 2021, 10, 1309.

19. Zhao, J.; Zhu, Y.; Zhang, B.; et al. Method of predicting SOH and RUL of lithium-ion battery based on the combination of LSTM and GPR. Sustainability 2022, 14, 11865.

20. Li, S.; Yang, S.; Jiang, Z.; Jiang, W.; Zhu, Z.; Ma, Y. An online state of charge estimation method: support vector machine battery model fusing a filtering algorithm. Electr. Power. Syst. Res. 2026, 253, 112530.

21. Qin, M.; Zhu, J.; Li, K.; Li, Q.; Liu, Y.; Su, T. Dynamic and uncertainty-aware isomorphic knowledge distillation for state of health estimation of lithium-ion batteries. J. Power. Sources. 2026, 678, 240066.

22. Peng, S.; Wang, Y.; Tang, A.; Jiang, Y.; Kan, J.; Pecht, M. State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries. Energy 2025, 315, 134293.

23. Ding, S.; Li, Y.; Dai, H.; Wang, L.; He, X. Accurate model parameter identification to boost precise aging prediction of lithium‐ion batteries: a review. Adv. Energy. Mater. 2023, 13, 2301452.

24. Asiri, M.; Kedhim, M.; Jain, V.; et al. Impact of temperature and state-of-charge on long-term storage degradation in lithium-ion batteries: an integrated P2D-based degradation analysis. RSC. Adv. 2025, 15, 22576-86.

25. Liu, B.; Tang, X.; Gao, F. Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model. Electrochim. Acta. 2020, 344, 136098.

26. An, Y.; Feng, F.; Luo, H.; et al. Fusion of local and global feature-based explainable diagnostic method for lithium-ion battery degradation modes. IEEE. Trans. Transp. Electrific. 2025, 11, 8353-64.

27. Kadiwala, S.; Savsaviya, P.; Pandey, S. V.; et al. Decoding degradation: The synergy of partial differential equations and advanced predictive models for lithium-ion battery. J. Power. Sources. 2025, 627, 235771.

28. Enrico, D. S.; Vanessa, P.; Massimiliano, L.; Antonello, R. Degradation mechanisms and differential curve modeling for non-invasive diagnostics of lithium cells: an overview. Renew. Sustain. Energy. Rev. 2025, 211, 115349.

29. Li, R.; Kirkaldy, N. D.; Oehler, F. F.; Marinescu, M.; Offer, G. J.; O’kane, S. E. J. The importance of degradation mode analysis in parameterising lifetime prediction models of lithium-ion battery degradation. Nat. Commun. 2025, 16, 2776.

30. Almunyif, A. A.; Mumtaz, S.; Afzal, A. M. Synergistic Ni(OH)₂@Li₄Ti₅O₁₂@B/N-doped graphene architecture: pioneering high-energy hybrid supercapacitors with superior electrocatalytic performance. Inorg. Chem. Commun. 2025, 181, 115243.

31. Yang, Y.; Chen, L.; Yang, L.; Du, X.; Yang, Y. Capacity fade characteristics of lithium iron phosphate cell during dynamic cycle. Energy 2020, 206, 118155.

32. Yvenat, M.; Chavillon, B.; Mayousse, E.; et al. Study of the influence of the formation protocol on the SEI layer formed at the graphite electrode surface of a non-aqueous potassium-ion hybrid supercapacitor (KIC) through STEM and XPS analyses. Sustain. Energy. Fuels. 2023, 7, 4150-9.

33. Qiu, F.; Wang, X.; Shi, H.; et al. Synergistic optimization of NCM9055/AC composites enabling superior performance in hybrid battery-supercapacitors. Energy. Mater. 2026, 6.

34. Hu, D.; Su, Y.; Chen, L.; et al. The mechanism of side reaction induced capacity fading of Ni-rich cathode materials for lithium ion batteries. J. Energy. Chem. 2021, 58, 1-8.

35. Li, X.; Li, Z.; Zhang, Y.; et al. Stable hexaazatrinaphthylene-based covalent organic framework as high-capacity electrodes for aqueous hybrid supercapacitors. Energy. Mater. 2025, 5, 500036.

36. Xu, L.; Lin, X.; Xie, Y.; Hu, X. Enabling high-fidelity electrochemical P2D modeling of lithium-ion batteries via fast and non-destructive parameter identification. Energy. Storage. Mater. 2022, 45, 952-68.