Developed to mitigate future risks associated with lithium resource scarcity, sodium-ion batteries constitute a new energy storage technology rooted in lithium-ion battery principles. For the simulation study of energy storage sodium-ion batteries to progress effectively within new power systems, reliable equivalent circuit models and precise state-of-charge estimation are indispensable. To address the parameter dependence on ambient temperature inherent in battery modeling, this study introduces modifications to the parameters and structural configuration of the conventional Thevenin model. These adaptations culminate in a temperature-enhanced model, engineered to boost temperature sensitivity and thereby improve the robustness of sodium-ion battery state-of-charge estimation across varying thermal conditions. Moreover, to actively counteract the detrimental effects of noise inherent in real-world operation, we incorporate an Adaptive Extended Kalman Filter technique within the battery model. This sophisticated algorithm dynamically adjusts its parameters, effectively suppressing unwanted noise perturbations and improving estimation fidelity. Consequently, the implementation of the proposed temperature enhancement model proves highly effective, offering excellent temperature sensitivity alongside high-precision and robust state-of-charge estimates critical for reliable management of energy storage sodium-ion batteries.