State of Charge Estimation of Ultracapacitor Modules Based on Sage Husa Improved Adaptive Extended Kalman Filter Algorithm

Conventional ultracapacitor parameter identification and SOC estimation algorithms suffer from slow computation speed and low robustness. To improve state estimation accuracy, this paper proposes a forgetting factor recursive least squares parameter identification algorithm based on a two-branch model. Building upon this foundation, an improved adaptive extended Kalman filter algorithm incorporating Sage-Husa estimation is utilized for SOC estimation, which enhances the robustness of state estimation while reducing computational load. Experimental results verify the stability and accuracy of the proposed algorithm. The results demonstrate that the combined algorithm improves both robustness and computational efficiency while maintaining estimation precision.