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All issues Volume 330 (2025) EPJ Web Conf., 330 (2025) 07003 References
Open Access
Issue
EPJ Web Conf.
Volume 330, 2025
The 5th International Conference on Electrical Sciences and Technologies in the Maghreb (CISTEM 2024)
Article Number 07003
Number of page(s) 7
Section Energy Transmission, Storage and Management
DOI https://doi.org/10.1051/epjconf/202533007003
Published online 30 June 2025
  1. Errifai, N., Rachid, A., Khamlichi, S., Saidi, E., Mortabit, I., El Fadil, H., Abbou, A.: Combined Coulomb-Counting and Open-Circuit Voltage Methods for State of Charge Estimation of Li-Ion Batteries. In: El Fadil, H. et Zhang, W. (éd.) Automatic Control and Emerging Technologies. p. [Google Scholar]
  2. Errifai, N., Rachid, A., Khamlichi, S., Saidi, E., Bella, H., Rachid, K., Lassioui, A.: Comparative Analysis of Electrical Equivalent Circuit Models for Lithium-Ion Batteries. In: Motahhir, S. et Bossoufi, B. (éd.) Digital Technologies and Springer Nature Switzerland, Cham (2024) [Google Scholar]
  3. Plett, G.L.: Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation. https://doi.org/10.1016/j.jpowsour.2004.02.033 [Google Scholar]
  4. Shrivastava, P., Kok Soon, T., Bin Idris, M.Y.I., Mekhilef, S., Adnan, S.B.R.S.: Combined State of Charge and State of Energy Estimation of Lithium-Ion Battery Using Dual Forgetting Factor-Based Adaptive Extended Kalman Filter for Electric Vehicle Applications. IEEE Transactions on Vehicular Technology. 70, https://doi.org/10.1109/TVT.2021.3051655 [Google Scholar]
  5. Priya, R.P., R, S., Sakile, R.: State of charge estimation of lithium-ion battery based on extended Kalman filter and unscented Kalman filter techniques. Energy Storage. 5, e408 (2023). https://doi.org/10.1002/est2.408 [CrossRef] [Google Scholar]
  6. Liu, Q., Yu, Q.: The lithium battery SOC estimation on square root unscented Kalman filter. https://doi.org/10.1016/j.egyr.2022.05.079 [Google Scholar]
  7. Chen, Z., Zhou, J., Zhou, F., Xu, S.: State-of-charge estimation of lithium-ion batteries based on improved H infinity filter algorithm and its novel equalization method. Journal of Cleaner Production. 290, 125180 (2021). https://doi.org/10.1016/j.jclepro.2020.125180 [CrossRef] [Google Scholar]
  8. Chen, Y., Li, R., Sun, Z., Zhao, L., Guo, X.: SOC estimation of retired lithium-ion batteries for electric vehicle with improved particle filter by H-(2023). https://doi.org/10.1016/j.egyr.2023.01.018 [Google Scholar]
  9. Sorouri, H., Oshnoei, A., Che, Y., Teodorescu, R.: A comprehensive review of hybrid battery state of charge estimation: Exploring physics-aware AI-based approaches. Journal of Energy Storage. 100, 113604 (2024). https://doi.org/10.1016/j.est.2024.113604 [CrossRef] [Google Scholar]
  10. D.v.s.r., S., Badachi, C., Green II, R.C.: A review on data-driven SOC estimation with Li-Ion batteries: Implementation methods & future aspirations. Journal of Energy Storage. 72, 108420 (2023). https://doi.org/10.1016/j.est.2023.108420 [CrossRef] [Google Scholar]
  11. Xia, Z., Wu, Y.: A hybrid network of NARX and DS-attention applied for the state estimation of lithium-ion batteries. International Journal of Electrochemical Science. 19, 100632 (2024). https://doi.org/10.1016/j.ijoes.2024.100632 [CrossRef] [Google Scholar]
  12. How, D.N.T., Hannan, M.A., Hossain Lipu, M.S., Ker, P.J.: State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review. IEEE Access. 7, https://doi.org/10.1109/ACCESS.2019.2942213 [Google Scholar]
  13. Li, F., Zuo, W., Zhou, K., Li, Q., Huang, Y., Zhang, G.: State-of-charge estimation of lithiumion battery based on second order resistor-capacitance circuit-PSO-TCN model. Energy. 289, 130025 (2024). https://doi.org/10.1016/j.energy.2023.130025 [CrossRef] [Google Scholar]
  14. Zeng, Y., Li, Y., Yang, T.: State of Charge Estimation for Lithium-Ion Battery Based on Unscented Kalman Filter and Long Short-Term Memory Neural Network. Batteries. 9, 358 (2023). https://doi.org/10.3390/batteries9070358 [CrossRef] [Google Scholar]
  15. Di Luca, G., Di Blasio, G., Gimelli, A., Misul, D.A.: Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles. Energies. 17, 202 (2024). https://doi.org/10.3390/en17010202 [Google Scholar]
  16. Ahmed, F., Abulsaud, K., Massoud, A.M.: On Equivalent Circuit Model-Based State-of-Charge Estimation for Lithium-ion Batteries in Electric Vehicles. IEEE Access. 1-1 (2025). https://doi.org/10.1109/ACCESS.2025.3560065 [Google Scholar]
  17. Merrouche, W., Lekouaghet, B., Bouguenna, E., Himeur, Y.: Parameter estimation of ECM model for Li-Ion battery using the weighted mean of vectors algorithm. Journal of Energy Storage. 76, 109891 (2024). https://doi.org/10.1016/j.est.2023.109891 [CrossRef] [Google Scholar]
  18. Hussein, H.M., Aghmadi, A., Abdelrahman, M.S., Rafin, S.M.S.H., Mohammed, O.: A review of battery state of charge estimation and management systems: Models and future prospective. Wiley Interdisciplinary Reviews: Energy and Environment. 13, e507 (2024). https://doi.org/10.1002/wene.507 [Google Scholar]
  19. Hashemzadeh, P., Désilets, M., Lacroix, M.: Online state estimation of Li-ion batteries using continuous-discrete nonlinear Kalman filters based on a nonlinear simplified electrochemical model. Electrochimica Acta. 481, 143953 (2024). https://doi.org/10.1016/j.electacta.2024.143953 [CrossRef] [Google Scholar]
  20. Bao, M., Liu, D., Wu, Y., Wang, Z., Yang, J., Lan, L., Ru, Q.: Interpretable machine learning prediction for li-on electrochemical impedance spectroscopy and temporal features. Electrochimica Acta. 494, 144449 (2024). https://doi.org/10.1016/j.electacta.2024.144449 [CrossRef] [Google Scholar]
  21. Xile, D., Caiping, Z., Jiuchun, J.: Evaluation of SOC Estimation Method Based on EKF/AEKF under Noise Interference. Energy Procedia. 152, [Google Scholar]
  22. Li, M., Zhang, Y., Hu, Z., Zhang, Y., Zhang, J.: A Battery SOC Estimation Method Based on AFFRLS-EKF. Sensors. 21, 5698 (2021). https://doi.org/10.3390/s21175698 [CrossRef] [Google Scholar]
  23. Luzi, M., Paschero, M., Rizzi, A., Maiorino, E., Frattale Mascioli, F.M.: A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells. IEEE Transactions on Neural Networks and Learning Systems. 30, https://doi.org/10.1109/TNNLS.2018.2827307 [Google Scholar]
  24. Olabi, A.G., Abdelghafar, A.A., Soudan, B., Alami, A.H., Semeraro, C., Al Radi, M., Al-Murisi, M., Abdelkareem, M.A.: Artificial neural network driven prognosis and estimation of Lithium-Ion battery states: Current insights and future perspectives. Ain Shams Engineering Journal. 15, 102429 (2024). https://doi.org/10.1016/j.asej.2023.102429 [CrossRef] [Google Scholar]
  25. Yang, F., Zhang, S., Li, W., Miao, Q.: State-of-charge estimation of lithium-ion batteries using LSTM and UKF. Energy. 201, 117664 (2020). https://doi.org/10.1016/j.energy.2020.117664 [CrossRef] [Google Scholar]
  26. Aung, H., Soon Low, K., Ting Goh, S.: State-of-Charge Estimation of Lithium-Ion Battery Using Square Root Spherical Unscented Kalman Filter (Sqrt-UKFST) in Nanosatellite. IEEE (2015). https://doi.org/10.1109/TPEL.2014.2361755 [Google Scholar]
  27. Rezaei, O., Rahdan, A., Sardari, S., Dahmardeh, M., Wang, Z.: A fuzzy robust two-stage unscented Kalman filter method for uncertainty and state of charge estimation of lithium-ion batteries. Journal of Energy Storage. 68, 107883 (2023). https://doi.org/10.1016/j.est.2023.107883 [CrossRef] [Google Scholar]
  28. Tu, H., Moura, S., Wang, Y., Fang, H.: Integrating physics-based modeling with machine learning for lithium-ion batteries. Applied Energy. 329, 120289 (2023). https://doi.org/10.1016/j.apenergy.2022.120289 [CrossRef] [Google Scholar]
  29. Zeng, Y., Li, Y., Yang, T.: State of Charge Estimation for Lithium-Ion Battery Based on Unscented Kalman Filter and Long Short-Term Memory Neural Network. Batteries. 9, 358 (2023). https://doi.org/10.3390/batteries9070358 [CrossRef] [Google Scholar]
  30. Li, F., Zuo, W., Zhou, K., Li, Q., Huang, Y., Zhang, G.: State-of-charge estimation of lithiumion battery based on second order resistor-capacitance circuit-PSO-TCN model. Energy. 289, 130025 (2024). https://doi.org/10.1016/j.energy.2023.130025 https://doi.org/10.1016/j.egypro.2018.09.204 [CrossRef] [Google Scholar]

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