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Robust hydrogen-consumption-minimization strategy based salp swarm algorithm for energy management of fuel cell/supercapacitor/batteries in highly fluctuated load condition

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  • Fathy, Ahmed
  • Rezk, Hegazy
  • Nassef, Ahmed M.

Abstract

This paper presents a hybrid power system suitable for powering electric cars, trains and aircraft especially under high fluctuated load demand. The hybrid system includes fuel cells (FC), batteries and supercapacitors (SCs). The energy management strategy (EMS) is a key factor to reduce the total hydrogen consumption and slow down the FC performance degradation. A new EMS based on a recent optimization technique named Salp Swarm Algorithm (SSA) is proposed taking into consideration that the load demand is fully satisfied within the constraints of each energy source. The main objective of the proposed strategy is to minimize the total hydrogen consumption of the system. To minimize the energy obtained from the FC, the energy supplied by the batteries and supercapacitors is maximized. The SSA is an efficient and simple optimizer that needs few numbers of control parameters to be adjusted compared to other optimization algorithms. In order to show the validity of the proposed approach, a comparative study with other conventional approaches such as classical proportional-integral control strategy, frequency decoupling, and state machine (FDSM) control approach, equivalent consumption minimization strategy (ECMS), external energy maximization strategy (EEMS), and genetic algorithm (GA) is presented. In this study, the capstones of the comparison are the total H2 consumption of the FC and the efficiency of the algorithm. The obtained results confirmed that the proposed SSA approach is superior and efficient than the other strategies.

Suggested Citation

  • Fathy, Ahmed & Rezk, Hegazy & Nassef, Ahmed M., 2019. "Robust hydrogen-consumption-minimization strategy based salp swarm algorithm for energy management of fuel cell/supercapacitor/batteries in highly fluctuated load condition," Renewable Energy, Elsevier, vol. 139(C), pages 147-160.
    • Handle: RePEc:eee:renene:v:139:y:2019:i:c:p:147-160
    DOI: 10.1016/j.renene.2019.02.076
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    References listed on IDEAS

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    1. Bizon, Nicu, 2017. "Energy optimization of fuel cell system by using global extremum seeking algorithm," Applied Energy, Elsevier, vol. 206(C), pages 458-474.
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    Cited by:

    1. Fathy, Ahmed & Yousri, Dalia & Alanazi, Turki & Rezk, Hegazy, 2021. "Minimum hydrogen consumption based control strategy of fuel cell/PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm," Energy, Elsevier, vol. 225(C).
    2. Ferahtia, Seydali & Djeroui, Ali & Rezk, Hegazy & Houari, Azeddine & Zeghlache, Samir & Machmoum, Mohamed, 2022. "Optimal control and implementation of energy management strategy for a DC microgrid," Energy, Elsevier, vol. 238(PB).
    3. Rudravaram Venkatasatish & Dhanamjayulu Chittathuru, 2023. "Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    4. Xueqin Lü, & Wu, Yinbo & Lian, Jie & Zhang, Yangyang, 2021. "Energy management and optimization of PEMFC/battery mobile robot based on hybrid rule strategy and AMPSO," Renewable Energy, Elsevier, vol. 171(C), pages 881-901.
    5. Ioan-Sorin Sorlei & Nicu Bizon & Phatiphat Thounthong & Mihai Varlam & Elena Carcadea & Mihai Culcer & Mariana Iliescu & Mircea Raceanu, 2021. "Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies," Energies, MDPI, vol. 14(1), pages 1-29, January.
    6. Benmouna, A. & Becherif, M. & Boulon, L. & Dépature, C. & Ramadan, Haitham S., 2021. "Efficient experimental energy management operating for FC/battery/SC vehicles via hybrid Artificial Neural Networks-Passivity Based Control," Renewable Energy, Elsevier, vol. 178(C), pages 1291-1302.
    7. Ferahtia, Seydali & Rezk, Hegazy & Olabi, A.G. & Alhumade, Hesham & Bamufleh, Hisham S. & Doranehgard, Mohammad Hossein & Abdelkareem, Mohammad Ali, 2022. "Optimal techno-economic multi-level energy management of renewable-based DC microgrid for commercial buildings applications," Applied Energy, Elsevier, vol. 327(C).
    8. Perez-Dávila, Oriana & Álvarez Fernández, Roberto, 2023. "Optimization algorithm applied to extended range fuel cell hybrid vehicles. Contribution to road transport decarbonization," Energy, Elsevier, vol. 267(C).
    9. Hu, Jianjun & Wang, Yangguang & Zou, Lingbo & Wang, Zhouxin, 2023. "Adaptive rule control strategy for composite energy storage fuel cell vehicle based on vehicle operating state recognition," Renewable Energy, Elsevier, vol. 204(C), pages 166-175.

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