Issue

Vol. 10, No. 4, November 2025

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The Application of Adaptive Neuro Fuzzy Inference System (ANFIS) Method in Estimating State of Charge (SOC) and State of Health (SOH) of Lithium-Ion Batteries

https://doi.org/10.22219/kinetik.v10i4.2357
Selamat Muslimin
Politeknik Negeri Sriwijaya
Ekawati Prihatini
Sriwijaya State Polytechnic
Nyayu Latifah Husni
Sriwijaya State Polytechnic
Wahyu Caesandra
Opole University of Technology

Corresponding Author(s) : Selamat Muslimin

selamet_muslimin@polsri.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 4, November 2025

Abstract

The increasing reliance on lithium-ion batteries (LIBs) for electric vehicles and portable electronics demands accurate monitoring of battery performance, particularly the State of Charge (SOC) and State of Health (SOH). Conventional estimation methods—such as Coulomb counting, Kalman filtering, and equivalent circuit modeling—face challenges under dynamic conditions due to drift and limited adaptability. Recent studies have explored machine learning and neuro-fuzzy approaches to enhance prediction accuracy, yet many lack integration of real-time hybrid learning or struggle with high estimation error in noisy data environments. This research aims to apply the Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate SOC and SOH using experimental data from a 48V lithium-ion battery. The novelty lies in combining voltage, current, and capacity data within a MATLAB-based ANFIS framework that employs a hybrid learning algorithm integrating backpropagation and Recursive Least Squares Estimation (RLSE). Training data for SOC estimation used charging voltage and current, while SOH estimation incorporated discharging data and capacity. Results show that ANFIS achieved high accuracy with RMSE of 0.1466 and MAE of 0.021 for SOC, and RMSE of 0.012 and MAE of 0.0017 for SOH. The estimated SOH was 33.61%, closely aligned with actual values. These findings confirm ANFIS as a robust and adaptive method for real-time battery diagnostics. Future work will explore multi-input hybrid models, the integration of IoT-based BMS telemetry, and testing across diverse battery chemistries to generalize the model's performance and extend its application in smart energy systems

Keywords

Adaptive Neuro Fuzzy Inference System (ANFIS) Lithium Ion Battery; State of Charge (SOC) State of Health (SOH)
Muslimin, S., Prihatini, E., Husni, N. L., & Caesandra, W. (2025). The Application of Adaptive Neuro Fuzzy Inference System (ANFIS) Method in Estimating State of Charge (SOC) and State of Health (SOH) of Lithium-Ion Batteries. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(4). https://doi.org/10.22219/kinetik.v10i4.2357
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. S. Manzetti and F. Mariasiu, “Electric vehicle battery technologies: From present state to future systems,” Renew. Sustain. Energy Rev., vol. 51, pp. 1004–1012, 2015, doi: 10.1016/j.rser.2015.07.010.
  2. A. J. Salkind, C. Fennie, P. Singh, T. Atwater, and D. E. Reisner, “Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology,” J. Power Sources, vol. 80, no. 1, pp. 293–300, 1999, doi: 10.1016/S0378-7753(99)00079-8
  3. T. Zahid, K. Xu, W. Li, C. Li, and H. Li, “State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles,” Energy, vol. 162, pp. 871–882, 2018, doi: 10.1016/j.energy.2018.08.071.
  4. J. P. Rivera-Barrera, N. Muñoz-Galeano, and H. O. Sarmiento-Maldonado, “SOC estimation for lithium-ion batteries: Review and future challenges,” Electronics, vol. 6, no. 4, p. 102, 2017, doi: 10.3390/electronics6040102.
  5. P. Venugopal and T. Vigneswaran, “State-of-charge estimation methods for Li-ion batteries in electric vehicles,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 7, pp. 37–46, 2019.
  6. M. A. Awadallah and B. Venkatesh, “Accuracy improvement of SOC estimation in lithium-ion batteries,” J. Energy Storage, vol. 6, pp. 95–104, 2016, doi: 10.1016/j.est.2016.03.003.
  7. D. N. T. How, M. A. Hannan, M. S. H. Lipu, and P. J. Ker, “State of charge estimation for lithium-ion batteries using model-based and data-driven methods: A review,” IEEE Access, vol. 7, pp. 136116–136136, 2019, doi: 10.1109/ACCESS.2019.2942213.
  8. P. Dini, A. Colicelli, and S. Saponara, “Review on modeling and SOC/SOH estimation of batteries for automotive applications,” Batteries, vol. 10, no. 1, 2024, doi: 10.3390/batteries10010034.
  9. [15] D. Selvabharathi and N. Muruganantham, “Battery health and performance monitoring system: A closer look at state of health (SOH) assessment methods of a lead-acid battery,” Indones. J. Electr. Eng. Comput. Sci., vol. 18, no. 1, pp. 261–267, 2019, doi: 10.11591/ijeecs.v18.i1.pp261-267.
  10. M. I. D. Prasetyo, A. Tjahjono, and N. A. Windarko, “Feed Forward Neural Network sebagai algoritma estimasi state of charge baterai lithium polymer,” Klik - Kumpul. J. Ilmu Komput., vol. 7, no. 1, p. 13, 2020, doi: 10.20527/klik.v7i1.290
  11. M. Muharrom, “Analisis komparasi algoritma data mining Naive Bayes, K-Nearest Neighbors dan regresi linier dalam prediksi harga emas,” J. Teknol. Inf., vol. 4, no. 4, pp. 430–438, 2023.
  12. N. Yang, X. Zhang, and G. Li, “State of charge estimation for pulse discharge of a LiFePO₄ battery by a revised Ah counting,” Electrochim. Acta, vol. 151, pp. 63–71, Jan. 2015, doi: 10.1016/j.electacta.2014.11.011.
  13. Z. Wang, H. Wei, Y. Xi, and G. Xiao, “Data-driven energy utilization for plug-in hybrid electric bus with driving pattern application and battery health considerations,” Energy, vol. 310, 133041, Nov. 2024. [Online]. Available: https://doi.org/10.1016/j.energy.2024.133041.
  14. A. Massing, “A closer look at state of charge (SOC) and state of health (SOH) estimation techniques for batteries,” Analog Devices, 2017.
  15. R. Xiong, J. Cao, Q. Yu, H. He, and F. Sun, “Critical review on the battery state of charge estimation methods for electric vehicles,” IEEE Access, vol. 6, pp. 1832–1843, 2017, doi: 10.1109/ACCESS.2017.2780258.
  16. J. Park, J. Lee, S. Kim, and I. Lee, “Real-time state of charge estimation for each cell of lithium battery pack using neural networks,” Appl. Sci., vol. 10, no. 5, 2020, [verify volume/issue if needed].
  17. T.-H. Cho, H.-R. Hwang, J.-H. Lee, and I.-S. Lee, “Battery state-of-charge estimation using ANN and ANFIS for photovoltaic system,” J. Korean Inst. Inf. Technol., vol. 18, no. 5, pp. 55–64, 2020, doi: 10.14801/jkiit.2020.18.5.55.
  18. T. Liu, D. Li, K. Wang, and Q. Lu, "Estimation of battery capacity degeneration based on an improved neural fuzzy inference system under dynamic operating conditions," Journal of Energy Storage, vol. 102, Part A, 113988, Nov. 2024. [Online]. Available: https://doi.org/10.1016/j.est.2024.113988.
  19. C. H. Cai, D. Du, and Z. Y. Liu, “Battery state-of-charge (SOC) estimation using adaptive neuro-fuzzy inference system (ANFIS),” in Proc. IEEE Int. Conf. Fuzzy Syst., vol. 2, pp. 1068–1073, 2003, doi: 10.1109/FUZZ.2003.1206580.
  20. G. Guo, P. Xu, Z. Bai, S. Zhou, G. Xu, and B. Cao, “Optimization of Ni-MH battery fast charging in electric vehicles using dynamic data mining and ANFIS,” in Lect. Notes Comput. Sci., vol. 5227, pp. 468–475, 2008, doi: 10.1007/978-3-540-85984-0_56.
  21. W. S. Kemal and M. Alhasa, Modeling of Tropospheric Delays Using ANFIS, Springer Briefs in Meteorology, 2016. [Online]. Available: http://www.springer.com/series/13553
  22. M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, 3rd ed. Pearson Education. [2019].
  23. J. Marchildon, M. L. Doumbia, and K. Agbossou, “SOC and SOH characterisation of lead acid batteries,” in Proc. IECON 2015 - 41st Annu. Conf. IEEE Ind. Electron. Soc., pp. 1442–1446, 2015, doi: 10.1109/IECON.2015.7392303.
  24. K. H. Chao and J. W. Chen, “State-of-health estimator based on extension theory with a learning mechanism for lead-acid batteries,” Expert Syst. Appl., vol. 38, no. 12, pp. 15183–15193, 2011, doi: 10.1016/j.eswa.2011.05.084.
  25. H. Yu, H. Zhang, Z. Zhang, and S. Yang, “State estimation of lithium-ion batteries via physics-machine learning combined methods: A methodological review and future perspectives,” eTransportation, vol. 24, 100420, May 2025. [Online]. Available: https://doi.org/10.1016/j.etran.2025.100420
  26. H. Yu, H. Zhang, Z. Zhang, and S. Yang, “State estimation of lithium-ion batteries via physics-machine learning combined methods: A methodological review and future perspectives,” eTransportation, vol. 24, 100420, May 2025. [Online]. Available: https://doi.org/10.1016/j.etran.2025.100420
  27. M. Waseem, G. S. Lakshmi, M. Amir, M. Ahmad, and M. Suhaib, “Advancement in battery health monitoring methods for electric vehicles: Battery modelling, state estimation, and internet-of-things based methods,” J. Power Sources, vol. 633, 236414, Mar. 2025. [Online]. Available: https://doi.org/10.1016/j.jpowsour.2025.236414.
  28. M. H. H. Khine, C. G. Kim, and N. Aunsri, “A review of Bayesian-filtering-based techniques in RUL prediction for Lithium-Ion batteries,” J. Energy Storage, vol. 111, 115371, Mar. 2025. [Online]. Available: https://doi.org/10.1016/j.est.2025.115371.
  29. K. W. E. Cheng, B. P. Divakar, H. Wu, K. Ding, and H. F. Ho, “Battery-management system (BMS) and SOC development for electrical vehicles,” IEEE Trans. Veh. Technol., vol. 60, no. 1, pp. 76–88, 2011, doi: 10.1109/TVT.2010.2089647.
  30. Z. Chen, Y. Peng, J. Shen, Q. Zhang, Y. Liu, Y. Zhang, X. Xia, and Y. Liu, "State of health estimation for lithium-ion batteries based on fragmented charging data and improved gated recurrent unit neural network," Journal of Energy Storage, vol. 115, 115952, Apr. 2025. [Online]. Available: https://doi.org/10.1016/j.est.2025.115952.
  31. F. Cempaka, A. Muid, and I. Ruslianto, “Rancang bangun lengan robot,” J. Coding, Sist. Komput. Untan, vol. 4, no. 1, pp. 32–42, 2016.
Read More
Author biographies is not available.
Download this PDF file
Statistic
Read Counter : 0

Downloads

Download data is not yet available.