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Vol. 10, No. 4, November 2025

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The Application of the 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
Sriwijaya State Polytechnic
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 the 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
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References
  1. SDing, Ning, Krishnamachar Prasad, and Tek Tjing Lie. "The electric vehicle: a review." International Journal of Electric and Hybrid Vehicles 9.1 (2017): 49-66. https://doi.org/10.1504/IJEHV.2017.082816
  2. Manikandan, P., et al. "Lithium Ion Battery Pack Robust State of Charge and Charging Method Estimation using Fuzzy Logic." 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/ICICCS65191.2025.10984683
  3. Tripp-Barba, Carolina, et al. "A Systematic Mapping Study on State Estimation Techniques for Lithium-Ion Batteries in Electric Vehicles." World Electric Vehicle Journal 16.2 (2025): 57. https://doi.org/10.3390/wevj16020057
  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. http://doi.org/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. Fan, Xichen, et al. "An improved Transformer incorporating fuzzy information entropy and average input strategy for SOC estimation of lithium-ion battery." Energy (2025): 136953. https://doi.org/10.1016/j.energy.2025.136953
  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. http://doi.org/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. http://doi.org/10.3390/batteries10010034
  9. Budiman, Arief S., et al. "Estimation of Lead Acid Battery Degradation–A Model for the Optimisation of Battery Energy Storage System using Machine Learning." (2025). https://doi.org/10.3390/electrochem6030033
  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. http://doi.org/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. https://doi.org/10.47065/bit.v4i4.986
  12. Vishwakarma, Vinod Kumar, Swapnil Srivastava, and Venktesh Mishra. "Strategies of battery management systems in electric vehicles: a review." International Journal of Modelling and Simulation (2025): 1-31. https://doi.org/10.1080/02286203.2025.2523065
  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. https://doi.org/10.1016/j.energy.2024.133041
  14. Soyoye, Babatunde D., et al. "State of charge and state of health estimation in electric vehicles: challenges, approaches and future directions." Batteries 11.1 (2025): 32. https://doi.org/10.3390/batteries11010032
  15. Pisani Orta, Miguel Antonio, David García Elvira, and Hugo Valderrama Blaví. "Review of State-of-Charge Estimation Methods for Electric Vehicle Applications." World Electric Vehicle Journal 16.2 (2025): 87. https://doi.org/10.3390/wevj16020087
  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. 23, 2020. https://doi.org/10.3390/app10238644
  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. http://doi.org/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. https://doi.org/10.1016/j.est.2024.113988
  19. Battery state-of-charge (SOC) estimation using adaptive neuro-fuzzy inference system (ANFIS
  20. An, Zhensheng, et al. "SOC Estimation Method Based on Variational Bayesian Recalibrated Unscented Kalman Filter Considering Error Compensation Strategy." Journal of The Electrochemical Society 172.7 (2025): 070536. http://doi.org/10.1149/1945-7111/adf0e6
  21. Mhady, Ahmed Abo, et al. "New Hybrid Models Integrating the Firefly Optimization Algorithm with the Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems to Improve Estimation at Completion." Journal of Construction Engineering and Management 151.11 (2025): 04025160. https://doi.org/10.1061/JCEMD4.COENG-16487
  22. Oguanobi, Nonso C., et al. "Modeling and kinetics investigation of adsorptive properties and regeneration of modified clay on azo dyes removal from aqueous solution using artificial intelligence (ANN, ANFIS) and RSM." Sigma Journal of Engineering and Natural Sciences 43.1 (2025): 316-339. http://doi.org/10.14744/sigma.2025.00023
  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. http://doi.org/10.1109/IECON.2015.7392303
  24. Budiman, Arief S., et al. "Estimation of Lead Acid Battery Degradation–A Model for the Optimisation of Battery Energy Storage System using Machine Learning." (2025). https://doi.org/10.3390/electrochem6030033
  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. 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. 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. 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. http://doi.org/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. https://doi.org/10.1109/TVT.2010.2089647
  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.
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