Exponential-Offset Modelling and XRD Correlation of SOH Degradation in LiFePO4 Batteries under Extreme Loading

Authors

  • Chaironi Latif Electrical Engineering Study Program, Telkom University, Surabaya Campus, East Java, Indonesia, 60231; Center of Excellence for Circular Ecosystem and Sustainable Technology, Research Institute for Intelligent Business and Sustainable Economy, Telkom University, Surabaya Campus, Surabaya, East Java, Indonesia, 60231
  • Tubagus Adam Jody Maulana Electrical Engineering Study Program, Telkom University, Surabaya Campus, East Java, Indonesia, 60231
  • Mohammad Fahmi Ilmi Yogianto Electrical Engineering Study Program, Telkom University, Surabaya Campus, East Java, 60231
  • Ahmad Fauzan Adziimaa Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, 60111; Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto, 860-8555, Japan

DOI:

https://doi.org/10.23960/jemit.379

Keywords:

State of Health (SOH), Exponential-Offset model, Extreme loading, X-ray Diffraction (XRD)

Abstract

Understanding the relationship between electrochemical degradation and structural changes is critical for improving the reliability of lithium-ion batteries. In this study, the state of health (SOH) of 18650-type lithium iron phosphate (LiFePO4, LFP) cells was evaluated under extreme loading using discharge resistances of 2.5 Ω and 0.005 Ω. The SOH decreased sharply after the first cycle and then declined more gradually, and the degradation trend was well described by an exponential-offset model with RMSE = 2.87, MAE = 2.25, and R2 = 0.90. Structural analysis was performed by X-ray diffraction (XRD) on electrode samples taken after one discharge at 2.5 Ω (L1), 100 discharges at 2.5 Ω (L100), and one discharge at 0.005 Ω (LD). The XRD results confirmed that the main phase was graphite, but with reduced peak intensities, peak broadening, and increased background noise, indicating crystallinity loss and partial amorphization. These findings demonstrate that SOH degradation is strongly correlated with the decline in crystallinity, and that extreme loading can trigger significant structural deterioration even within a single discharge.

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References

Hasan, M. M., Haque, R., Jahirul, M. I., Rasul, M. G., Fattah, I. M. R., Hassan, N. M. S., & Mofijur, M. (2025). Advancing energy storage: The future trajectory of lithium-ion battery technologies. Journal of Energy Storage, 120, 116511. https://doi.org/10.1016/j.est.2025.116511

Huang, B., Liao, H., Wang, Y., Liu, X., & Yan, X. (2021). Prediction and evaluation of health state for power battery based on Ridge linear regression model. Science Progress, 104(4). https://doi.org/10.1177/00368504211059047

Kano, K., Segi, T., Ozono, H., & Koga, K. (2025). Interpretable deep learning for XRD pattern analysis in lithium-ion batteries CNN and attention-based feature extraction. Journal of Power Sources, 655, 237944. https://doi.org/10.1016/j.jpowsour.2025.237944

Latif, C., Fahmi, M., Yogianto, I., Hafidz, I., & Fauzan, A. (2025). Sustainable Lithium Battery Development in Indonesia: The Role of Natural Materials and Recycling Processes in Future Challenges. Journal of Energy, Material, and Instrumentation Technology, 6(3). https://doi.org/10.23960/jemit.347

Lei, X., Xie, F., Wang, J., & Zhang, C. (2024). A review of lithium-ion battery state of health and remaining useful life estimation methods based on bibliometric analysis. Journal of Traffic and Transportation Engineering (English Edition), 11(6), 1420–1446. https://doi.org/10.1016/j.jtte.2024.09.004

Ngoy, K. R., Lukong, V. T., Yoro, K. O., Makambo, J. B., Chukwuati, N. C., Ibegbulam, C., Eterigho-Ikelegbe, O., Ukoba, K., & Jen, T.-C. (2025). Lithium-ion batteries and the future of sustainable energy: A comprehensive review. Renewable and Sustainable Energy Reviews, 223, 115971. https://doi.org/10.1016/j.rser.2025.115971

Pender, J. P., Jha, G., Youn, D. H., Ziegler, J. M., Andoni, I., Choi, E. J., Heller, A., Dunn, B. S., Weiss, P. S., Penner, R. M., & Mullins, C. B. (2020). Electrode Degradation in Lithium-Ion Batteries. ACS Nano, 14(2), 1243–1295. https://doi.org/10.1021/acsnano.9b04365

Rouhi, H., Karola, E., Serna-Guerrero, R., & Santasalo-Aarnio, A. (2021). Voltage behavior in lithium-ion batteries after electrochemical discharge and its implications on the safety of recycling processes. Journal of Energy Storage, 35, 102323. https://doi.org/10.1016/j.est.2021.102323

Sivalertporn, K., Poopanya, P., & Phophongviwat, T. (2025). Capacity Forecasting of Lithium-Ion Batteries Using Empirical Models: Toward Efficient SOH Estimation with Limited Cycle Data. Energies, 18(14), 3828. https://doi.org/10.3390/en18143828

Sun, S., Guan, T., Cheng, X., Zuo, P., Gao, Y., Du, C., & Yin, G. (2018). Accelerated aging and degradation mechanism of LiFePO 4 /graphite batteries cycled at high discharge rates. RSC Advances, 8(45), 25695–25703. https://doi.org/10.1039/C8RA04074E

Wang, L., Qiu, J., Wang, X., Chen, L., Cao, G., Wang, J., Zhang, H., & He, X. (2022). Insights for understanding multiscale degradation of LiFePO4 cathodes. EScience, 2(2), 125–137. https://doi.org/10.1016/j.esci.2022.03.006

Wang, Y., Guo, S., Cui, Y., Deng, L., Zhao, L., Li, J., & Wang, Z. (2025). A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges. Renewable and Sustainable Energy Reviews, 224, 116125. https://doi.org/10.1016/j.rser.2025.116125

Wei, Y., & Wu, D. (2024). State of health and remaining useful life prediction of lithium-ion batteries with conditional graph convolutional network. Expert Systems with Applications, 238, 122041. https://doi.org/10.1016/j.eswa.2023.122041

Yogianto, M. F. I. (2025). Karakterisasi Sifat Listrik Dan Struktur Material Katoda Baterai LiFePO4 Sebagai Dasar Pengembangan Recycle Baterai [Universitas Telkom, S1 Teknik Elektro]. https://openlibrary.telkomuniversity.ac.id/home/catalog/id/231377/slug/karakterisasi-sifat-listrik-dan-struktur-material-katoda-baterai-lifepo4-sebagai-dasar-pengembangan-recycle-baterai-dalam-bentuk-buku-karya-ilmiah.html

Yogianto, M. F. I., Faricha, A., & Latif, C. (2025). Characterisation of Electrical Properties and Structural Material of LiFePO 4 Cathodes Under Various Conditioning Treatments. 2025 International Electronics Symposium (IES), 90–95. https://doi.org/10.1109/IES67184.2025.11160817

Zhang, X., Sun, P., Wang, S., & Zhu, Y. (2023). Experimental Study of the Degradation Characteristics of LiFePO4 and LiNi0.5Co0.2Mn0.3O2 Batteries during Overcharging at Low Temperatures. Energies, 16(6), 2786. https://doi.org/10.3390/en16062786

Zheng, B., Duan, Y., Fu, Q., Xiang, S., Gao, P., Osman, Z., & Liu, J. (2025). Differential voltage analysis for rapid and sensitive rate capability evaluation of graphite anodes. Transactions of Materials Research, 1(4), 100065. https://doi.org/10.1016/j.tramat.2025.100065

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Published

2025-11-16

How to Cite

Latif, C., Maulana, T. A. J., Yogianto, M. F. I., & Adziimaa, A. F. (2025). Exponential-Offset Modelling and XRD Correlation of SOH Degradation in LiFePO4 Batteries under Extreme Loading. Journal of Energy, Material, and Instrumentation Technology, 6(4), 174–181. https://doi.org/10.23960/jemit.379