A Novel Supercapacitor Model Parameters Identification Method Using Metaheuristic Gradient-Based Optimization Algorithms

Ahmad Yasin, Rached Dhaouadi () and Shayok Mukhopadhyay
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Ahmad Yasin: Mechatronics Graduate Program, College of Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates
Rached Dhaouadi: Department of Electrical Engineering, College of Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates
Shayok Mukhopadhyay: Electrical & Computer Engineering and Computer Science Department, University of New Haven, West Haven, CT 06516, USA

Energies, 2024, vol. 17, issue 6, 1-31

Abstract: This paper addresses the critical role of supercapacitors as energy storage systems with a specific focus on their modeling and identification. The lack of a standardized and efficient method for identifying supercapacitor parameters has a definite effect on widespread adoption of supercapacitors, especially in high-power density applications like electric vehicle regenerative braking. The study focuses on parameterizing the Zubieta model for supercapacitors, which involves identifying seven parameters using a hybrid metaheuristic gradient-based optimization (MGBO) approach. The effectiveness of the MGBO method is compared to the existing particle swarm optimization (PSO) and to the following algorithms proposed and developed in this work: ‘modified MGBO’ (M-MGBO) and two PSO variations—one combining PSO and M-MGBO and the other incorporating a local escaping operator (LCEO) with PSO. Metaheuristic- and gradient-based algorithms are both affected by problems associated with locally optimal results and with issues related to enforcing constraints/boundaries on solution values. This work develops the above-mentioned innovations to the MGBO and PSO algorithms for addressing such issues. Rigorous experimentation considering various types of input excitation provides results indicating that hybrid PSO-MGBO and PSO-LCEO outperform traditional PSO, showing improvements of 51% and 94%, respectively, while remaining comparable to M-MGBO. These hybrid approaches effectively estimate Zubieta model parameters. The findings highlight the potential of hybrid optimization strategies in enhancing precision and effectiveness in supercapacitor model parameterization.

Keywords: supercapacitor; Zubieta model; energy storage; particle swarm optimization; gradient-based optimization; metaheuristic algorithms; local escaping operator; parameter identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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