QunYuThis email address is being protected from spambots. You need JavaScript enabled to view it. and Jing Xie

The Open University of Sichuan, Sichuan Huaxin Modern Vocational College, Chengdu 610073, Sichuan, China


 

Received: March 15, 2025
Accepted: August 31, 2025
Publication Date: October 18, 2025

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202605_29(5).0025  


Battery management systems rely on an accurate estimate of the state of charge of lithiumion battery for safe and reliable operations. In this paper, we present a Luenberger observer suitable for nonlinear battery dynamics that are subject to practical conditions- Lipschitz continuity, input faults, and disturbances. Our observer differs from the existing body of literature by considering the aforementioned conditions explicitly, which allows for robust fault estimation and disturbance rejection. In our example, we show how our approach can reduce the state of charge estimation error by up to X% and reduce the convergence time by Y% when compared to other variants of the extended Kalman filter. Reliability and responsiveness are critical for battery management systems and the results presented in this paper represent an improvement to the quality of estimation, which has far reaching implications for safer and more efficient energy storage solutions in electric vehicles or grid applications.


Keywords: State of charge, Lithium-ion battery, Luenberger, Lipschitz, convergence time.


  1. [1] Z. Cui, L. Wang, Q. Li, and K. Wang, (2022) “A com prehensive review on the state of charge estimation for lithium-ion battery based on neural network" International Journal of Energy Research 46: 5423–5440. DOI: https: //doi.org/10.1002/er.7545.
  2. [2] P. Shrivastava, P. A. Naidu, S. Sharma, B. K. Panigrahi, and A. Garg, (2023) “Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications" Journal of Energy Storage 64: 107159. DOI: https: //doi.org/10.1016/j.est.2023.107159.
  3. [3] L. Zhou, X. Lai, B. Li, Y. Yao, M. Yuan, J. Weng, and Y. Zheng, (2023) “State estimation models of lithium-ion batteries for battery management system: status, challenges, and future trends" Batteries 9: 131. DOI: https: //doi.org/10.3390/batteries9020131.
  4. [4] O. Rezaei and M. Faghih, (2023) “Design of a Robust Unknown Input Observer for the State of Charge Estimation for Lithium-Ion Batteries" Advances in Engineering and Intelligence Systems 2: 28–36. DOI: https: //doi.org/10.22034/aeis.2023.397366.1099
  5. [5] K. S. Ng, C.-S. Moo, Y.-P. Chen, and Y.-C. Hsieh, (2009) “Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries" Applied energy 86: 1506–1511. DOI: https: //doi.org/10.1016/j.apenergy.2008.11.021.
  6. [6] J. Xie, J. Ma, and K. Bai, (2018) “Enhanced coulomb counting method for state-of-charge estimation of lithium ion batteries based on peukert’s law and coulombic efficiency" Journal of Power Electronics 18: 910–922. DOI: http: //dx.doi.org/10.6113/JPE.2018.18.3.910.
  7. [7] F. Mohammadi, (2022) “Lithium-ion battery State of-Charge estimation based on an improved Coulomb Counting algorithm and uncertainty evaluation" Journal of energy storage 48: 104061. DOI: https: //doi.org/10.1016/j.est.2022.104061
  8. [8] N. Christian and L. Ling, (2023) “A Dual Extended Kalman Filter for the State of Charge Estimation of Lithium ion Batteries" Advances in Engineering and Intelligence Systems 2: 1–10. DOI: https: //doi.org/10.22034/aeis.2023.412040.1123.
  9. [9] S. Lee, J. Kim, J. Lee, and B. H. Cho, (2008) “State of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge" Journal of power sources 185: 1367–1373. DOI: https: //doi.org/10.1016/j.jpowsour.2008.08.103
  10. [10] R. Xiong, Q. Yu, and C. Lin, (2017) “A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter" Applied energy 207: 346–353. DOI: https: //doi.org/10.1016/j.apenergy.2017.05.136.
  11. [11] F. Zheng, Y. Xing, J. Jiang, B. Sun, J. Kim, and M. Pecht, (2016) “Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries" Applied energy 183: 513–525. DOI: https: //doi.org/10.1016/j.apenergy.2016.09.010.
  12. [12] J. Xie, X. Wei, X. Bo, P. Zhang, P. Chen, W. Hao, and M. Yuan, (2023) “State of charge estimation of lithium ion battery based on extended Kalman filter algorithm" Frontiers in Energy Research 11: 1180881. DOI: https: //doi.org/10.3389/fenrg.2023.1180881.
  13. [13] L. Duan, X. Zhang, Z. Jiang, Q. Gong, Y. Wang, and X. Ao, (2023) “State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis" Energy 280: 128159. DOI: https: //doi.org/10.1016/j.energy.2023.128159.
  14. [14] C. Wang, S. Wang, J. Zhou, J. Qiao, X. Yang, and Y. Xie, (2023) “A novel back propagation neural network dual extended Kalman filter method for state-of-charge and state-of-health co-estimation of lithium-ion batteries based on limited memory least square algorithm" Journal of Energy Storage 59: 106563. DOI: https: //doi.org/10.1016/j.est.2022.106563.
  15. [15] M. Li, C. Li, Q. Zhang, W. Liao, and Z. Rao, (2023) “State of charge estimation of Li-ion batteries based on deep learning methods and particle-swarm-optimized Kalman filter" Journal of Energy Storage 64: 107191. DOI: https: //doi.org/10.1016/j.est.2023.107191.
  16. [16] J. Yun, Y. Choi, J. Lee, S. Choi, and C. Shin, (2023) “State-of-charge estimation method for lithium-ion batteries using extended kalman filter with adaptive battery parameters" IEEE Access 11: 90901–90915. DOI: https://doi.org/10.1109/ACCESS.2023.3305950.
  17. [17] S. Zhang, C. Zhang, S. Jiang, and X. Zhang, (2022) “A comparative study of different adaptive ex tended/unscented Kalman filters for lithium-ion battery state-of-charge estimation" Energy 246: 123423. DOI: https: //doi.org/10.1016/j.energy.2022.123423.
  18. [18] C. Jiang, S. Wang, B. Wu, C. Fernandez, X. Xiong, and J. Coffie-Ken, (2021) “A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter" Energy 219: 119603. DOI: https: //doi.org/10.1016/j.energy.2020.119603.
  19. [19] S. Yang, S. Zhou, Y. Hua, X. Zhou, X. Liu, Y. Pan, H. Ling, and B. Wu, (2021) “A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter" Scientific reports 11: 5805. DOI: https: //doi.org/10.1038/s41598-021-84729-1.
  20. [20] Y. Xu, M. Hu, A. Zhou, Y. Li, S. Li, C. Fu, and C. Gong, (2020) “State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter" Applied Mathematical Modelling 77: 1255–1272. DOI: https: //doi.org/10.1016/j.apm.2019.09.011.
  21. [21] X. Yang, S. Wang, W. Xu, J. Qiao, C. Yu, P. Takyi Aninakwa, and S. Jin, (2022) “A novel fuzzy adaptive cubature Kalman filtering method for the state of charge and state of energy co-estimation of lithium-ion batteries" Electrochimica Acta 415: 140241. DOI: https: //doi.org/10.1016/j.electacta.2022.140241
  22. [22] N. Peng, S. Zhang, X. Guo, and X. Zhang, (2021) “On line parameters identification and state of charge estimation for lithium-ion batteries using improved adaptive dual unscented Kalman filter" International Journal of Energy Research 45: 975–990. DOI: https: //doi.org/10.1002/er.6088.
  23. [23] S. Zhang, X. Guo, and X. Zhang, (2020) “An improved adaptive unscented kalman filtering for state of charge on line estimation of lithium-ion battery" Journal of energy storage 32: 101980. DOI: https: //doi.org/10.1016/j.est.2020.101980
  24. [24] D. Wang, Y. Yang, and T. Gu, (2023) “A hierarchical adaptive extended Kalman filter algorithm for lithium-ion battery state of charge estimation" Journal of Energy Storage 62: 106831. DOI: https: //doi.org/10.1016/j.est.2023.106831.
  25. [25] C. Jiang, S. Wang, B. Wu, C. Fernandez, X. Xiong, and J. Coffie-Ken, (2021) “A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter" Energy 219: 119603. DOI: https: //doi.org/10.1016/j.energy.2020.119603.
  26. [26] H. Zhao, C. Liao, C. Zhang, L. Wang, and L. Wang, (2024) “State-of-charge estimation of lithium-ion battery: Joint long short-term memory network and adaptive ex tended Kalman filter online estimation algorithm" Journal of Power Sources 604: 234451. DOI: https://doi. org/10.1016/j.jpowsour.2024.234451.
  27. [27] D. N. T. How, M. A. Hannan, M. S. H. Lipu, and P. J. Ker, (2019) “State of charge estimation for lithium ion batteries using model-based and data-driven methods: Areview" Ieee Access 7: 136116–136136. DOI: https: //doi.org/10.1109/ACCESS.2019.2942213.
  28. [28] Z.Zou,J.Xu,C.Mi,B.Cao,andZ.Chen,(2014)“Evaluation of model based state of charge estimation methods for lithium-ion batteries" Energies 7: 5065–5082. DOI: https: //doi.org/10.3390/en7085065.
  29. [29] P. Shrivastava, T. K. Soon, M. Y. I. B. Idris, and S. Mekhilef, (2019) “Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries" Renewable and Sustainable Energy Reviews 113: 109233. DOI: https: //doi.org/10.1016/j.rser.2019.06.040
  30. [30] H. Obeid, R. Petrone, H. Chaoui, and H. Gualous, (2022) “Higher order sliding-mode observers for state-of charge and state-of-health estimation of lithium-ion batteries" IEEE Transactions on Vehicular Technology 72: 4482–4492. DOI: https: //doi.org/10.1109/TVT.2022.3226686.
  31. [31] M. Chen, F. Han, L. Shi, Y. Feng, C. Xue, W. Gao, and J. Xu, (2022) “Sliding mode observer for state-of-charge estimation using hysteresis-based Li-ion battery model" Energies 15: 2658. DOI: https: //doi.org/10.3390/en15072658.
  32. [32] A. Fereydooni, E. Vafa, M. R. Pishvaie, and B. G. Choobar, (2023) “Robust adaptive sliding mode observer for core temperature and state of charge monitoring of Li-ion battery: A simulation study" Journal of Energy Storage 70: 107960. DOI: https: //doi.org/10.1016/j.est.2023.107960.
  33. [33] M. Zhou, K. Wei, X. Wu, L. Weng, H. Su, D. Wang, Y. Zhang, and J. Li, (2023) “Fractional-order sliding mode observers for the estimation of state-of-charge and state-of-health of lithium batteries" Batteries 9: 213. DOI: https: //doi.org/10.3390/batteries9040213.
  34. [34] A. Shah, K. Shah, C. Shah, and M. Shah, (2022) “State of charge, remaining useful life and knee point estimation based on artificial intelligence and Machine learning in lithium-ion EV batteries: A comprehensive review" Renewable Energy Focus 42: 146–164. DOI: https: //doi.org/10.1016/j.ref.2022.06.001.
  35. [35] Z. Cui, L. Wang, Q. Li, and K. Wang, (2022) “A com prehensive review on the state of charge estimation for lithium-ion battery based on neural network" Interna tional Journal of Energy Research 46: 5423–5440. DOI: https: //doi.org/10.1002/er.7545.
  36. [36] J. Li, M. Ye, W. Meng, X. Xu, and S. Jiao, (2020) “A novel state of charge approach of lithium ion battery using least squares support vector machine" IEEE Access 8: 195398–195410. DOI: https: //doi.org/10.1109/ACCESS.2020.3033451
  37. [37] S. Sepasi, L. R. Roose, and M. M. Matsuura, (2015) “Extended Kalman filter with a fuzzy method for accurate battery pack state of charge estimation" Energies 8: 5217 5233. DOI: https: //doi.org/10.3390/en8065217