REFERENCES

1. Wu, L.; Pang, K.; Zheng, Y.; Huang, P.; Chen, Y. A multi-module equalization system for lithium-ion battery packs. Int. J. Energy. Res. 2022, 46, 2771-82.

2. Lai, X.; Gao, W.; Zheng, Y.; et al. A comparative study of global optimization methods for parameter identification of different equivalent circuit models for Li-ion batteries. Electrochim. Acta. 2019, 295, 1057-66.

3. IEA. Global EV outlook 2024. https://iea.blob.core.windows.net/assets/a9e3544b-0b12-4e15-b407-65f5c8ce1b5f/GlobalEVOutlook2024.pdf. (accessed 2024-01-14).

4. Wang, W.; Wu, Y. An overview of recycling and treatment of spent LiFePO₄ batteries in China. Resour. Conserv. Recycl. 2017, 127, 233-43.

5. Dou, Y.; Song, X.; Zhuang, X.; Wu, W.; Fan, S. Life cycle assessment and carbon reduction scenario analysis of retired lithium iron phosphate batteries for cascade utilization. China. Environ. Sci. 2024, 44, 4091-100.

6. S, V.; Che, H. S.; Selvaraj, J.; et al. State of health (SoH) estimation methods for second life lithium-ion battery - review and challenges. Appl. Energy. 2024, 369, 123542.

7. Liu, W.; Xu, Y. Data-driven online health estimation of Li-ion batteries using a novel energy-based health indicator. IEEE. Trans. Energy. Convers. 2020, 35, 1715-8.

8. Lai, X.; Qiao, D.; Zheng, Y.; Ouyang, M.; Han, X.; Zhou, L. A rapid screening and regrouping approach based on neural networks for large-scale retired lithium-ion cells in second-use applications. J. Clean. Prod. 2019, 213, 776-91.

9. Jiang, Y.; Jiang, J.; Zhang, C.; Zhang, W.; Gao, Y.; Guo, Q. Recognition of battery aging variations for LiFePO₄ batteries in 2nd use applications combining incremental capacity analysis and statistical approaches. J. Power. Sources. 2017, 360, 180-8.

10. Lai, X.; Deng, C.; Li, J.; Zhu, Z.; Han, X.; Zheng, Y. Rapid sorting and regrouping of retired lithium-ion battery modules for echelon utilization based on partial charging curves. IEEE. Trans. Veh. Technol. 2021, 70, 1246-54.

11. Yin, H.; Li, Y.; Kang, Y.; Zhang, C. A two-stage sorting method combining static and dynamic characteristics for retired lithium-ion battery echelon utilization. J. Energy. Storage. 2023, 64, 107178.

12. Kumar, J.; Neiber, R. R.; Park, J.; et al. Recent progress in sustainable recycling of LiFePO₄-type lithium-ion batteries: strategies for highly selective lithium recovery. Chem. Eng. J. 2022, 431, 133993.

13. Nyamathulla, S.; Dhanamjayulu, C. A review of battery energy storage systems and advanced battery management system for different applications: challenges and recommendations. J. Energy. Storage. 2024, 86, 111179.

14. Demirci, O.; Taskin, S.; Schaltz, E.; Acar, D. B. Review of battery state estimation methods for electric vehicles - Part I: SOC estimation. J. Energy. Storage. 2024, 87, 111435.

15. Luo, F.; Lyu, T.; Wang, D.; Zheng, Z. A review on green and sustainable carbon anodes for lithium ion batteries: utilization of green carbon resources and recycling waste graphite. Green. Chem. 2023, 25, 8950-69.

16. Ali, M. U.; Zafar, A.; Nengroo, S. H.; Hussain, S.; Junaid, A. M.; Kim, H. Towards a smarter battery management system for electric vehicle applications: a critical review of lithium-ion battery state of charge estimation. Energies 2019, 12, 446.

17. Lee, J.; Won, J. Enhanced coulomb counting method for SoC and SoH estimation based on coulombic efficiency. IEEE. Access. 2023, 11, 15449-59.

18. Zhu, Y.; Xiong, Y.; Xiao, J.; Yi, T.; Li, C.; Sun, Y. An improved coulomb counting method based on non-destructive charge and discharge differentiation for the SOC estimation of NCM lithium-ion battery. J. Energy. Storage. 2023, 73, 108917.

19. Braco, E.; San, M. I.; Sanchis, P.; Ursúa, A. Fast capacity and internal resistance estimation method for second-life batteries from electric vehicles. Appl. Energy. 2023, 329, 120235.

20. Duong, V.; Bastawrous, H. A.; Lim, K.; See, K. W.; Zhang, P.; Dou, S. X. Online state of charge and model parameters estimation of the LiFePO₄ battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares. J. Power. Sources. 2015, 296, 215-24.

21. Ahmeid, M.; Muhammad, M.; Lambert, S.; Attidekou, P. S.; Milojevic, Z. A rapid capacity evaluation of retired electric vehicle battery modules using partial discharge test. J. Energy. Storage. 2022, 50, 104562.

22. Mendoza-Hernandez, O. S.; Ishikawa, H.; Nishikawa, Y.; Maruyama, Y.; Sone, Y.; Umeda, M. State of charge dependency of graphitized-carbon-based reactions in a lithium-ion secondary cell studied by electrochemical impedance spectroscopy. Electrochim. Acta. 2014, 131, 168-73.

23. Galeotti, M.; Cinà, L.; Giammanco, C.; Cordiner, S.; Di, C. A. Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy. Energy 2015, 89, 678-86.

24. Zhang, W.; Li, T.; Wu, W.; Ouyang, N.; Huang, G. Data-driven state of health estimation in retired battery based on low and medium-frequency electrochemical impedance spectroscopy. Measurement 2023, 211, 112597.

25. Fan, W.; Jiang, B.; Wang, X.; et al. Enhancing capacity estimation of retired electric vehicle lithium-ion batteries through transfer learning from electrochemical impedance spectroscopy. eTransportation 2024, 22, 100362.

26. Luo, F.; Huang, H.; Ni, L.; Li, T. Rapid prediction of the state of health of retired power batteries based on electrochemical impedance spectroscopy. J. Energy. Storage. 2021, 41, 102866.

27. Fahmy, H. M.; Hasanien, H. M.; Alsaleh, I.; Ji, H.; Alassaf, A. State of health estimation of lithium-ion battery using dual adaptive unscented Kalman filter and Coulomb counting approach. J. Energy. Storage. 2024, 88, 111557.

28. Oji, T.; Zhou, Y.; Ci, S.; Kang, F.; Chen, X.; Liu, X. Data-driven methods for battery SOH estimation: survey and a critical analysis. IEEE. Access. 2021, 9, 126903-16.

29. Camboim, M. M.; Moreira, A. C.; Rosolem, M. D. F. N.; et al. State of health estimation of second-life batteries through electrochemical impedance spectroscopy and dimensionality reduction. J. Energy. Storage. 2024, 78, 110063.

30. Xiong, X.; Wang, Y.; Jiang, C.; Zhang, X.; Xiang, H.; Chen, Z. End-to-end deep learning powered battery state of health estimation considering multi-neighboring incomplete charging data. Energy 2024, 292, 130495.

31. Deng, Z.; Lin, X.; Cai, J.; Hu, X. Battery health estimation with degradation pattern recognition and transfer learning. J. Power. Sources. 2022, 525, 231027.

32. Yang, Y.; Xu, Y.; Nie, Y.; et al. Deep transfer learning enables battery state of charge and state of health estimation. Energy 2024, 294, 130779.

33. Yang, F.; Wang, D.; Zhao, Y.; Tsui, K.; Bae, S. J. A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries. Energy 2018, 145, 486-95.

34. Li, X.; Wang, T.; Pei, L.; Zhu, C.; Xu, B. A comparative study of sorting methods for lithium-ion batteries. In: 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), Beijing, China, August 31 - September 03, 2014; IEEE; 2014; p. 1-6.

35. Chen, Z.; Deng, Y.; Li, H.; Liu, W. An efficient regrouping method of retired lithium-ion iron phosphate batteries based on incremental capacity curve feature extraction for echelon utilization. J. Energy. Storage. 2022, 56, 105917.

36. He, Z.; Wu, X.; Li, X.; et al. The LiFePO₄ battery sorting method based on temperature analysis. E3S. Web. Conf. 2021, 236, 01031.

37. Zhang, W.; Wang, R.; Zhou, J.; Feng, Y. Study of a comprehensive evaluation method based on the screening of retired batteries. Chin. J. Electron. Devices. 2023, 46, 1049-55.

38. Garg, A.; Yun, L.; Gao, L.; Putungan, D. B. Development of recycling strategy for large stacked systems: experimental and machine learning approach to form reuse battery packs for secondary applications. J. Clean. Prod. 2020, 275, 124152.

39. Liu, F.; Chen, J.; Qin, D.; Wang, T. Research on appearance detection, sorting, and regrouping technology of retired batteries for electric vehicles. Sustainability 2023, 15, 15523.

40. Wang, Y.; Chen, K. Classification of aged batteries based on capacity and/or resistance through machine learning models with aging features as input: a comparative study. J. Clean. Prod. 2024, 471, 143431.

41. Liao, Q.; Mu, M.; Zhao, S.; et al. Performance assessment and classification of retired lithium ion battery from electric vehicles for energy storage. Int. J. Hydrogen. Energy. 2017, 42, 18817-23.

42. Qiang, H.; Liu, Y.; Zhang, W. A screening method for retired lithium-ion batteries based on support vector machine with a multi-class kernel function. J. Electrochem. Energy. Convers. Storage. 2024, 21, 021005.

43. Zhao, G.; He, M.; Tang, G.; Li, R.; Lu, L. Research on re-usage evaluation method of retired lithium-ion power batteries. Chin. J. Power. Sources. 2018, 42, 1632-4,1671.

44. Zhao, H.; Chen, Z.; Shu, X.; et al. Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization. Reliab. Eng. Syst. Saf. 2024, 243, 109798.

45. Li, C.; Liu, X.; Wang, C.; et al. Electrochemical-thermal behaviors of retired power lithium-ion batteries during high-temperature and overcharge/over-discharge cycles. Case. Stud. Therm. Eng. 2024, 61, 104898.

46. Gu, P.; Zhang, Y.; Duan, B.; Zhang, C.; Kang, Y. Rapid and flexible lithium-ion battery performance evaluation using random charging curve based on deep learning. Energy 2024, 293, 130746.

47. Ling, X.; Zhang, Q.; Xiang, Y.; Chen, J. S.; Peng, X.; Hu, X. A Cu/Ni alloy thin-film sensor integrated with current collector for in-situ monitoring of lithium-ion battery internal temperature by high-throughput selecting method. Int. J. Heat. Mass. Transf. 2023, 214, 124383.

48. Liu, T.; Chen, X.; Peng, Q.; Peng, J.; Meng, J. An enhanced sorting method for retired battery with feature selection and multiple clustering. J. Energy. Storage. 2024, 87, 111422.

49. Xu, Z.; Wang, J.; Lund, P. D.; et al. A novel clustering algorithm for grouping and cascade utilization of retired Li-ion batteries. J. Energy. Storage. 2020, 29, 101303.

50. Liu, X.; Tang, Q.; Feng, Y.; Lin, M.; Meng, J.; Wu, J. Fast sorting method of retired batteries based on multi-feature extraction from partial charging segment. Appl. Energy. 2023, 351, 121930.

51. Tian, A.; Wang, Y.; Yu, H.; et al. A novelty state of charge estimation framework for LiFePO₄ batteries considering multi-dimensional features selection. J. Energy. Storage. 2024, 101, 113829.

52. Pan, R.; Xiao, X.; Fan, J.; et al. Multi-stage deep sorting strategy for retired batteries based on the clustering of static and dynamic features. J. Energy. Storage. 2024, 99, 113387.

53. Lyu, Z.; Zhang, Y.; Wang, G.; Gao, R. A semiparametric clustering method for the screening of retired Li-ion batteries from electric vehicles. J. Energy. Storage. 2023, 63, 107030.

54. Li, C.; Wang, N.; Li, W.; Li, Y.; Zhang, J. Regrouping and echelon utilization of retired lithium-ion batteries based on a novel support vector clustering approach. IEEE. Trans. Transp. Electrific. 2022, 8, 3648-58.

55. Jiang, T.; Sun, J.; Wang, T.; et al. Sorting and grouping optimization method for second-use batteries considering aging mechanism. J. Energy. Storage. 2021, 44, 103264.

56. Ran, A.; Liang, Z.; Chen, S.; et al. Fast clustering of retired lithium-ion batteries for secondary life with a two-step learning method. ACS. Energy. Lett. 2022, 7, 3817-25.

57. Ran, A.; Chen, S.; Zhang, S.; et al. A gradient screening approach for retired lithium-ion batteries based on X-ray computed tomography images. RSC. Adv. 2020, 10, 19117-23.

58. Lin, M.; Wu, J.; Meng, J.; Wang, W.; Wu, J. Screening of retired batteries with gramian angular difference fields and ConvNeXt. Eng. Appl. Artif. Intell. 2023, 123, 106397.

59. Fu, Y.; Xu, J.; Shi, M.; Mei, X. A fast impedance calculation-based battery state-of-health estimation method. IEEE. Trans. Ind. Electron. 2022, 69, 7019-28.

60. Song, Z.; Zhang, H.; Jia, J. Data-driven state of health interval prediction for lithium-ion batteries. Electronics 2024, 13, 3991.

61. Ren, Z.; Du, C. A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries. Energy. Rep. 2023, 9, 2993-3021.

62. Bhattacharjee, A.; Verma, A.; Mishra, S.; Saha, T. K. Estimating state of charge for xEV batteries using 1D convolutional neural networks and transfer learning. IEEE. Trans. Veh. Technol. 2021, 70, 3123-35.

63. Kim, S.; Choi, Y. Y.; Kim, K. J.; Choi, J. Forecasting state-of-health of lithium-ion batteries using variational long short-term memory with transfer learning. J. Energy. Storage. 2021, 41, 102893.

64. Li, J.; Chen, E.; Ding, Z.; Zhu, L.; Lu, K.; Shen, H. T. Maximum density divergence for domain adaptation. IEEE. Trans. Pattern. Anal. Mach. Intell. 2021, 43, 3918-30.

65. Yayan, U.; Arslan, A. T.; Yucel, H. A novel method for SoH prediction of batteries based on stacked LSTM with quick charge data. Appl. Artif. Intell. 2021, 35, 421-39.