344-26

Real-Time Estimation of Polarization Dynamics and Stress Regimes in Lithium-Ion Batteries Based on Thevenin Equivalent Model and
Extended Kalman Filter

Mauricio Mendes da Silva(1), Mario Lucio da Silva Martins(1), Conrado Fleck dos Santos(2), Éder Bridi(2), Sergio Pires(2)

1. UFSM — Universidade Federal de Santa Maria, Santa Maria–RS, Brasil

2. UTEC — Universidad Tecnológica, Rivera, Uruguay

2026-06-27

This paper presents an online method for estimating the internal polarization level of lithium-ion batteries based on a
Thevenin equivalent circuit model. The proposed approach focuses on the dynamic polarization branch, which reflects
transient electrochemical effects associated with diffusion and relaxation phenomena. An energy-based formulation is introduced to characterize the behaviour of the polarization branch in terms of stored and dissipated energy, providing a physically meaningful indicator of internal battery stress. An Extended Kalman Filter (EKF) is employed to estimate the polarization voltage and model parameters in real-time, enabling the tracking of internal dynamic states under variable current profiles. Based on the estimated polarization energy, indicators are defined to identify electrochemical stress regimes related to aggressive operating conditions. The proposed method is validated through simulation and/or experimental data, demonstrating its applicability to battery monitoring and management in power and energy quality applications.

Key words: Renewable energy, power quality, battery modelling, internal polarization.

[1] G. L. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background, Journal of Power Sources, 2004.

[2] G. L. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification, Journal of Power Sources, 2004.

[3] G. L. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation, Journal of Power Sources, 2004.

[4] G. L. Plett, Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs, Journal of Power Sources, 2006.

[5] G. L. Plett, Battery Management Systems, Volume I: Battery Modeling, Artech House, 2015.

[6] X. Hu, S. Li, and H. Peng, A comparative study of equivalent circuit models for Li-ion batteries, Journal of Power Sources, 2012.

[7] J. Sun, G. Wei, M. Pecht, State-of-charge estimation of lithiumion batteries using adaptive extended Kalman filter, Journal of Power Sources, 2011.

[8] R. Xiong, J. Cao, Q. Yu, H. He, and F. Sun, Critical reviewon the battery state of charge estimation methods, IEEE Access,2018.

[9] S. Santhanagopalan and R. E. White, Online estimation of the state of charge of a lithium ion cell, Journal of Power Sources, 2006.

[10] C. R. Gould et al., Battery internal resistance estimation using Kalman filtering, IEEE Transactions on Control Systems Technology, 2009.4.