The state of energy (SoE) is a critical indicator for energy management in supercapacitor (SC) energy storage systems. The estimation accuracy of the SoE relies on the model fidelity, which means that the model parameters are required to be identified online to mitigate the aging effect. However, since the SC model is naturally nonlinear and high dimensional, it is typically difficult to identify the model parameters online. To address this issue, in this paper, we propose a generalized extended state observer (GESO) for SC SoE estimation based on an online identified model. A nonlinear mathematical model for SC is established based on the three branch equivalent circuit models, where the model parameters are online estimated with a designed modified recursive least square. A GESO is designed to estimate the SoE of SC in real time. A laboratory test bed has been built to verify the effectiveness of the proposed method. The experiment results show that the proposed method provides a better SoE estimation accuracy than the existing methods.
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