Advanced Kalman Filter–Based Approaches for State-of-Charge Estimation in Lithium-Ion Batteries: Models, Techniques, and Applications

DOI

10.2991/978-94-6463-821-9_32How to use a DOI?

Keywords

Lithium-Ion Batteries; State of Charge; Battery Management Systems; Kalman Filter; Neural Networks

Abstract

Lithium-ion batteries are pivotal in modern energy storage systems due to their high energy density and longevity, where accurate state-of-charge (SOC) estimation is critical for ensuring safe and efficient battery management. This paper reviews advancements in Kalman Filter (KF)-based SOC estimation methods, emphasizing their adaptability to nonlinear dynamics, parameter uncertainties, and aging effects. Classical KF demonstrates computational simplicity for mildly nonlinear systems, while Extended KF (EKF) and Unscented KF (UKF) address stronger nonlinearities via linearization or sigma-point propagation. Adaptive variants, such as the Model-Adaptive EKF (MAEKF), dynamically recalibrate resistance parameters using voltage-derivative analysis, reducing SOC errors below 4% in aged cells. Hybrid approaches integrating neural networks, such as LSTM-augmented UKF, achieve robust performance (1.1–2% RMSE) under dynamic loads and temperature fluctuations by leveraging temporal dependencies. However, challenges persist in computational complexity, temperature compensation, and chemistry-specific calibration. The study underscores the importance of balancing accuracy, adaptability, and computational efficiency for real-time battery management systems. These advancements enhance the reliability of SOC estimation in electric vehicles and grid storage, while future work should prioritize hybrid algorithms combining physical models with data-driven techniques and optimized embedded implementations for broader applicability.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Yuxuan Shi
PY  - 2025
DA  - 2025/08/31
TI  - Advanced Kalman Filter–Based Approaches for State-of-Charge Estimation in Lithium-Ion Batteries: Models, Techniques, and Applications
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 292
EP  - 302
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_32
DO  - 10.2991/978-94-6463-821-9_32
ID  - Shi2025
ER  -