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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ahmad Yasin & Rached Dhaouadi & Shayok Mukhopadhyay, 2024. "A Novel Supercapacitor Model Parameters Identification Method Using Metaheuristic Gradient-Based Optimization Algorithms," Energies, MDPI, vol. 17(6), pages 1-31, March.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1500-:d:1361271
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    References listed on IDEAS

    as
    1. Enas Taha Sayed & Abdul Ghani Olabi & Abdul Hai Alami & Ali Radwan & Ayman Mdallal & Ahmed Rezk & Mohammad Ali Abdelkareem, 2023. "Renewable Energy and Energy Storage Systems," Energies, MDPI, vol. 16(3), pages 1-26, February.
    2. Rabeh Abbassi & Salem Saidi & Abdelkader Abbassi & Houssem Jerbi & Mourad Kchaou & Bilal Naji Alhasnawi, 2023. "Accurate Key Parameters Estimation of PEMFCs’ Models Based on Dandelion Optimization Algorithm," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    3. Lei Zhang & Zhenpo Wang & Fengchun Sun & David G. Dorrell, 2014. "Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter," Energies, MDPI, vol. 7(5), pages 1-14, May.
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