Accurate and stable state estimation is critical for battery management. Although dual Kalman filtering can jointly estimate states and parameters, the strong coupling between filters may cause divergence under large initialization errors or model mismatch. This paper proposes a Stability Guaranteed Dual Kalman Filtering (SG-DKF) method. A Lyapunov-based analysis yields a sufficient stability condition, leading to an adaptive dead-zone rule that suspends parameter updates when the innovation exceeds a stability bound. Applied to an electrochemical battery model, SG-DKF achieves accuracy comparable to a dual EKF and reduces state of charge RMSE by over 45% under large initial state errors.
@article{arxiv.2512.04885,
title = {Stability-Guaranteed Dual Kalman Filtering for Electrochemical Battery State Estimation},
author = {Feng Guo and Guangdi Hu and Keyi Liao and Luis D. Couto and Khiem Trad and Ru Hong and Hamid Hamed and Mohammadhosein Safari},
journal= {arXiv preprint arXiv:2512.04885},
year = {2025}
}
Comments
This work has been submitted to 23rd IFAC World Congress for possible publication