JOINT FRACTIONAL-ORDER MODELING AND KALMAN FILTERING FOR BATTERY STATE-OF-CHARGE ESTIMATION
DOI:
https://doi.org/10.59277/RRST-EE.2026.3.13Keywords:
Lithium-ion battery, Fractional-order model, Extended Kalman filter, Parameter identification, Grünwald–Letnikov derivativeAbstract
Accurate state-of-charge (SOC) estimation is critical for reliable battery management. To address the limitations of conventional models, this paper presents a robust adaptive estimation framework that combines fractional-order modeling with extended Kalman filtering (EKF). The approach uses a computationally efficient fractional-order equivalent circuit model (FO-ECM) to represent nonlinear battery behavior. Its key innovation is augmenting the EKF’s state vector to jointly estimate SOC and slowly varying internal resistances, enabling real-time model adaptation to aging and temperature changes without separate health monitoring. Validated under dynamic loads and varying temperatures, the framework achieves SOC estimation with root mean square error (RMSE) consistently below 3.5% and robust convergence despite noise and initial uncertainty.
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