Hybrid osprey-parrot optimization algorithm for enhanced parameter identification of supercapacitor model

Supercapacitors (SCs) are important energy storage devices with widespread applications in various fields. These applications include renewable energy systems and electric vehicles. The ability of the SCs to deliver high power density, as well as their rapid charge-discharge cycles, makes them necessary. However, accurately modeling SCs remains challenging due to their complex dynamic behavior. A simple series RC circuit can approximate their characteristics. However, such a simple model fails to capture transient and long-term responses effectively. An advanced model incorporating three RC branches has been introduced to overcome this limitation. The first branch incorporates a capacitance that varies with voltage to account for immediate responses. The second branch models behavior within a short time frame. Meanwhile, the third branch accounts for long-term dynamics beyond 10 min. This comprehensive framework comprises eight distinct variables that should be optimally identified to achieve accurate performance prediction. A novel optimization approach is proposed to achieve accurate parameter identification by combining the Osprey Optimization Algorithm (OOA) and the Parrot Optimizer (PO). The hybrid approach has used the OOA's strong exploration capabilities in addition to the PO's efficient local search. This hybridization resulted in enhanced optimization performance. The proposed method is evaluated against other metaheuristic algorithms, including Genetic Algorithm (GA), Walrus Optimization Algorithm (WOA), and standalone OOA and PO. The performance is evaluated and compared based on the sum squared error (SSE) between experimental and simulated terminal voltage. The proposed hybrid approach achieves the lowest SSE value of 0.000173293. This confirms the superior accuracy. Statistical analysis further verified the robustness and reliability of the proposed hybrid algorithm. The strong correlation between model simulation results and experimental data highlights the effectiveness of the proposed hybrid optimization method for supercapacitor parameter identification. • This paper presents a novel model of the supercapacitor. • A hybrid Osprey-Parrot optimization algorithm is used for parameter estimation. • The fitness function relies on SSE between experimental and estimated voltage. • The results of proposed supercapacitor model are compared with experimental data. • The results of proposed model are compared with other recent models.