Estudio comparativo aplicado de métodos existentes para estimación del estado de carga en baterías de iones de litio
Barra lateral del artículo
Contenido principal del artículo
Resumen
Estimar el estado de carga (SOC) de las baterías de iones de litio es crucial para la operación de diversos dispositivos y equipos eléctricos y electrónicos. Este trabajo presenta la implementación de modelos basados en un enfoque bayesiano mediante el filtro de Kalman linealizado y el filtro de partículas (PF) para estimar el SOC en baterías de iones de litio. La ecuación de estado de los modelos bayesianos incorpora la resistencia de la batería como un parámetro de evolución artificial. De igual manera, se implementan dos modelos basados en algoritmos de aprendizaje automático: random forest y KNN, mediante el ajuste de parámetros de un circuito eléctrico equivalente a las curvas de mediciones de espectroscopía de impedancia electroquímica. Se utilizó una batería cilíndrica LCO tipo 26650. Los resultados muestran un alto desempeño en la estimación del SOC para los filtros bayesianos, entre los cuales el PF presenta las mejores métricas, con un coeficiente de determinación R2 de 0.9968.
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
La Universidad Politécnica Salesiana de Ecuador conserva los derechos patrimoniales (copyright) de las obras publicadas y favorecerá la reutilización de las mismas. Las obras se publican en la edición electrónica de la revista bajo una licencia Creative Commons Reconocimiento / No Comercial-Sin Obra Derivada 4.0 Ecuador: se pueden copiar, usar, difundir, transmitir y exponer públicamente.
El autor/es abajo firmante transfiere parcialmente los derechos de propiedad (copyright) del presente trabajo a la Universidad Politécnica Salesiana del Ecuador, para las ediciones impresas.
Se declara además haber respetado los principios éticos de investigación y estar libre de cualquier conflicto de intereses.
El autor/es certifican que este trabajo no ha sido publicado, ni está en vías de consideración para su publicación en ninguna otra revista u obra editorial.
El autor/es se responsabilizan de su contenido y de haber contribuido a la concepción, diseño y realización del trabajo, análisis e interpretación de datos, y de haber participado en la redacción del texto y sus revisiones, así como en la aprobación de la versión que finalmente se remite en adjunto.
Referencias
[1] R. Hema and M. J. Venkatarangan, “Advancing sustainable development: Introducing a novel fast charging technique for li-ion batteries with supercapacitor integration,” Computers and Electrical Engineering, vol. 120, p. 109810, 2024. [Online]. Available: https://doi.org/10.1016/j.compeleceng.2024.109810
[2] D. V. S. R. Sesidhar, C. Badachi, and R. C. Green II, “A review on data-driven SOC estimation with Li-ion batteries: Implementation methods & future aspirations,” Journal of Energy Storage, vol. 72, p. 108420, 2023. [Online]. Available: https://doi.org/10.1016/j.est.2023.108420
[3] Y. Tavakol-Moghaddam, M. Boroushaki, and M. Astaneh, “Reinforcement learning for battery energy management: A new balancing approach for Li-ion battery packs,” Results in Engineering, vol. 23, p. 102532, 2024. [Online]. Available: https://doi.org/10.1016/j.rineng.2024.102532
[4] O. Demirci, S. Taskin, E. Schaltz, and B. Acar Demirci, “Review of battery state estimation methods for electric vehicles - Part I: SOC estimation,” Journal of Energy Storage, vol. 87, p. 111435, 2024. [Online]. Available: https://doi.org/10.1016/j.est.2024.111435
[5] Q. Wang, J. Wang, P. Zhao, J. Kang, F. Yan, and C. Du, “Correlation between the model accuracy and model-based SOC estimation,” Electrochimica Acta, vol. 228, pp. 146–159, 2017. [Online]. Available: https://doi.org/10.1016/j.electacta.2017.01.057
[6] A. Tabine, E. M. Laadissi, H. Mastouri, A. Elachhab, S. Bouzaid, and A. Hajjaji, “A novel fitting polynomial approach for an accurate SOC estimation in Li-ion batteries in view of temperature variations,” Results in Engineering, p. 103962, 2025. [Online]. Available: https://doi.org/10.1016/j.rineng.2025.103962
[7] C. Zou, L. Zhang, X. Hu, Z. Wang, T. Wik, and M. Pecht, “A review of fractional-order techniques applied to lithiumion batteries, lead-acid batteries, and supercapacitors,”
Journal of Power Sources, vol. 390, pp. 286–296, 2018. [Online]. Available: https://doi.org/10.1016/j.jpowsour.2018.04.033
[8] C. Xu, T. Cleary, and H. K. Fathy, “Improving Li-S battery SOC estimation using an SOC-dependent resistance model,” IFACPapersOnLine, vol. 56, no. 3, pp. 439–444, 2023, 3rd Modeling, Estimation and Control Conference MECC 2023. [Online]. Available: https://doi.org/10.1016/j.ifacol.2023.12.063
[9] H. Ren, L. Jia, L. Yin, C. Dang, and Z. Chen, “Research on battery pack SOC consistency based on the electric-thermal-fluid coupling model,” Journal of Energy Storage, vol. 97, p. 112924, 2024. [Online]. Available: https://doi.org/10.1016/j.est.2024.112924
[10] A. C. Lazanas and M. I. Prodromidis, “Electrochemical impedance spectroscopy—A tutorial,” ACS Measurement Science Au, vol. 3, no. 3, pp. 162–193, 2023. [Online]. Available: https://doi.org/10.1021/acsmeasuresciau.2c00070
[11] H. Mustafa, C. Bourelly, M. Vitelli, F. Milano, M. Molinara, and L. Ferrigno, “SOC estimation on Li-ion batteries: A new EIS-based dataset for data-driven applications,” Data in Brief, vol. 57, p. 110947, 2024. [Online]. Available: https://doi.org/10.1016/j.dib.2024.110947
[12] C. Wang, M. Yang, X. Wang, Z. Xiong, F. Qian, C. Deng, C. Yu, Z. Zhang, and X. Guo, “A review of battery SOC estimation based on equivalent circuit models,” Journal of Energy Storage, vol. 110, p. 115346, 2025. [Online]. Available: https://doi.org/10.1016/j.est.2025.115346
[13] E. Paccha-Herrera, W. R. Calderón-Muñoz, M. Orchard, F. Jaramillo, and K. Medjaher, “Thermal modeling approaches for a LiCoO2 lithium-ion battery—A comparative study with experimental validation,” Batteries, vol. 6, no. 3, 2020. [Online]. Available: https://doi.org/10.3390/batteries6030040
[14] E. Almaita, S. Alshkoor, E. Abdelsalam, and F. Almomani, “State of charge estimation for a group of lithium-ion batteries using long shortterm memory neural network,” Journal of Energy Storage, vol. 52, p. 104761, 2022. [Online]. Available: https://doi.org/10.1016/j.est.2022.104761
[15] M. K. Al-Alawi, A. Jaddoa, J. Cugley, and H. Hassanin, “A novel enhanced SOC estimation method for lithium-ion battery cells using cluster-based LSTM models and centroid proximity selection,” Journal of Energy Storage, vol. 97, p. 112866, 2024. [Online]. Available: https://doi.org/10.1016/j.est.2024.112866
[16] G. Chen, W. Peng, and F. Yang, “An LSTM-SA model for SOC estimation of lithiumion batteries under various temperatures and aging levels,” Journal of Energy Storage, vol. 84, p. 110906, 2024. [Online]. Available: https://doi.org/10.1016/j.est.2024.110906
[17] Z. Cui, W. Hu, G. Zhang, Z. Zhang, and Z. Chen, “An extended Kalman filter based SOC estimation method for Li-ion battery,” Energy Reports, vol. 8, pp. 81–87, 2022, iCPE 2021 - The 2nd International Conference on Power Engineering. [Online]. Available: https://doi.org/10.1016/j.egyr.2022.02.116
[18] Y. Chen, R. Li, Z. Sun, L. Zhao, and X. Guo, “SOC estimation of retired lithium-ion batteries for electric vehicle with improved particle filter by H-infinity filter,” Energy Reports, vol. 9, pp. 1937–1947, 2023. [Online]. Available: https://doi.org/10.1016/j.egyr.2023.01.018
[19] N. Meddings, M. Heinrich, F. Overney, J.-S. Lee, V. Ruiz, E. Napolitano, S. Seitz, G. Hinds, R. Raccichini, M. Gaberšček, and J. Park, “Application of electrochemical impedance spectroscopy to commercial Li-ion cells: A review,” Journal of Power Sources, vol. 480, p. 228742, 2020. [Online]. Available: https://doi.org/10.1016/j.jpowsour.2020.228742
[20] E. Buchicchio, A. De Angelis, F. Santoni, P. Carbone, F. Bianconi, and F. Smeraldi, “Battery SOC estimation from EIS data based on machine learning and equivalent circuit model,” Energy, vol. 283, p. 128461, 2023. [Online]. Available: https://doi.org/10.1016/j.energy.2023.128461
[21] Y. Miao and Z. Gao, “Estimation for state of charge of lithium-ion batteries by adaptive fractional-order unscented Kalman filters,” Journal of Energy Storage, vol. 51, p. 104396, 2022. [Online]. Available: https://doi.org/10.1016/j.est.2022.104396
[22] A. Barai, K. Uddin, W. D. Widanage, A. Mc-Gordon, and P. Jennings, “A study of the influence of measurement timescale on internal resistance characterisation methodologies for lithium-ion cells,” Scientific Reports, vol. 8, no. 1, pp. 1–13, 2018. [Online]. Available: https://doi.org/10.1038/s41598-017-18424-5
[23] H. Thomas and M. H. Weatherspoon, “Capacity and state-of-health prediction of lithium-ion batteries using reduced equivalent circuit models,” Batteries, vol. 11, no. 4, 2025. [Online]. Available: https://doi.org/10.3390/batteries11040162
[24] X. Zhang, L. Zhang, J. Wu, W. Bai, H. Dai, H. Lin, F. Zhang, and Y. Yang, “SOC estimation of lithium-ion batteries using equivalent circuit model and Nyquist plots from EIS data: A machine learning approach,” Journal of Electroanalytical Chemistry, vol. 987, p. 119093, 2025. [Online]. Available: https://doi.org/10.1016/j.jelechem.2025.119093
[25] D. Simon, Optimal State Estimation: Kalman, HInfinity, and Nonlinear Approaches. John Wiley & Sons, 2006. [Online]. Available: http://doi.org/10.1002/0470045345
[26] P. Zarchan and H. Musoff, Fundamentals of Kalman Filtering: A Practical Approach, 4th ed. American Institute of Aeronautics and Astronautics, Inc., 2015. [Online]. Available: https://doi.org/10.2514/4.102776
[27] M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, 3rd ed. John Wiley & Sons, 2008. [Online]. Available: https://doi.org/10.1002/9780470377819
[28] D. Pola, H. Navarrete, M. Orchard, R. Rabie, M. Cerda, B. Olivares, J. Silva, P. Espinoza, and A. Perez, “Particle-filtering-based discharge time prognosis for lithium-ion batteries with a statistical characterization of use profiles,” IEEE Transactions on Reliability, vol. 64, pp. 1–11, 2015. [Online]. Available: https://doi.org/10.1109/TR.2014.2385069
[29] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174–188, 2002. [Online]. Available: https://doi.org/10.1109/78.978374
[30] C. Díaz, V. Quintero, A. Pérez, F. Jaramillo, C. Burgos-Mellado, H. Rozas, M. E. Orchard, D. Sáez, and R. Cárdenas, “Particle-filteringbased prognostics for the state of maximum power available in lithium-ion batteries at electromobility applications,” IEEE Trans. Veh. Technol., vol. 69, no. 7, pp. 7187–7200, 2020. [Online]. Available: https://doi.org/10.1109/TVT.2020.2993949
[31] M. Zaj¸ac, “Online fault detection of a mobile robot with a parallelized particle filter,” Neurocomputing., vol. 126, pp. 151–165, 2014. [Online]. Available: https://doi.org/10.1016/j.neucom.2012.11.049
[32] M. Sami Fadali, Introduction to random signals, estimation theory, and Kalman filtering, 1st ed. Springer Singapore, 2024. [Online]. Available: https://doi.org/10.1007/978-981-99-8063-5
[33] A. Tulsyan, B. Huang, R. Bhushan Gopaluni, and J. Fraser Forbes, “On simultaneous on-line state and parameter estimation in non-linear state-space models,” J. Process Control., vol. 23, no. 4, pp. 516–526, 2013. [Online]. Available: https://doi.org/10.1016/j.jprocont.2013.01.010
[34] Z. Hong, L. Xu, and J. Chen, “Artificial evolution based cost-reference particle filter for nonlinear state and parameter estimation in process systems with unknown noise statistics and model parameters,” J. Taiwan Inst. Chem. Eng., vol. 112, pp. 377–387, 2020. [Online]. Available: https://doi.org/10.1016/j.jtice.2020.04.009
[35] Y. Hu, P. Baraldi, F. Di Maio, and E. Zio, “Online performance assessment method for a modelbased prognostic approach,” IEEE Trans. Reliab., vol. 65, no. 2, pp. 718–735, 2016. [Online]. Available: https://doi.org/10.1109/TR.2015.2500681
[36] M. E. Orchard, F. A. Tobar, and G. J. Vachtsevanos, “Outer Feedback Correction Loops in Particle Filtering-based Prognostic Algorithms: Statistical Performance Comparison,” Stud. Informatics Control., vol. 18, no. 4, pp. 295–304, 2009. [Online]. Available: https://upsalesiana.ec/ing36ar10r36
[37] M. O. Oloyede, G. A. Akpakwu, H. C. Myburgh, A. De Freitas, and T. Kunatsa, “A review on state-of-charge estimation methods, energy storage technologies and state-of-the-art simulators: Recent developments and challenges,” World Electric Vehicle Journal, vol. 15, no. 9, 2024. [Online]. Available: https://doi.org/10.3390/wevj15090381
[38] A. Valade, P. Acco, P. Grabolosa, and J.-Y. Fourniols, “A study about kalman filters applied to embedded sensors,” Sensors, vol. 17, no. 12, 2017. [Online]. Available: https://doi.org/10.3390/s17122810
[39] G. Nishanth, M. M. Krishnan, B. Parandhaman, and J. Harinarayanan, “Hardware implementation of EKF based SOC estimate for lithium-ion batteries in electric vehicle applications,” Scientific Reports, vol. 15, no. 1, p. 15551, 2025. [Online]. Available: https://doi.org/10.1038/s41598-025-99931-8
[40] J. Michalski, M. Retinger, P. Kozierski, and J. Zietkiewicz, “Temperature control unit— modeling and implementation of a particle filter on a microcontroller,” Applied Sciences, vol. 12, no. 15, 2022. [Online]. Available: https://doi.org/10.3390/app12157631
[41] J. Schönefeld and D. Möller, “Mathematical aspects of the implementation of particle filters on FPGA,” IFAC Proceedings Volumes, vol. 45, no. 2, pp. 1243–1248, 2012, 7th Vienna International Conference on Mathematical Modelling. [Online]. Available: https://doi.org/10.3182/20120215-3-AT-3016.00220