IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v139y2019icp147-160.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119302307
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.02.076?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Gao, Qinxiang & Lei, Tao & Yao, Wenli & Zhang, Xingyu & Zhang, Xiaobin, 2023. "A health-aware energy management strategy for fuel cell hybrid electric UAVs based on safe reinforcement learning," Energy, Elsevier, vol. 283(C).
    8. 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).
    9. 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).
    10. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    2. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    4. Nicu Bizon & Phatiphat Thounthong, 2021. "A Simple and Safe Strategy for Improving the Fuel Economy of a Fuel Cell Vehicle," Mathematics, MDPI, vol. 9(6), pages 1-29, March.
    5. Daeichian, Abolghasem & Ghaderi, Razieh & Kandidayeni, Mohsen & Soleymani, Mehdi & Trovão, João P. & Boulon, Loïc, 2021. "Online characteristics estimation of a fuel cell stack through covariance intersection data fusion," Applied Energy, Elsevier, vol. 292(C).
    6. 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.
    7. Song, Dongran & Tu, Yanping & Wang, Lei & Jin, Fangjun & Li, Ziqun & Huang, Chaoneng & Xia, E & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Hoon Joo, Young, 2022. "Coordinated optimization on energy capture and torque fluctuation of wind turbines via variable weight NMPC with fuzzy regulator," Applied Energy, Elsevier, vol. 312(C).
    8. Bizon, Nicu, 2018. "Optimal operation of fuel cell/wind turbine hybrid power system under turbulent wind and variable load," Applied Energy, Elsevier, vol. 212(C), pages 196-209.
    9. Bizon, Nicu, 2019. "Hybrid power sources (HPSs) for space applications: Analysis of PEMFC/Battery/SMES HPS under unknown load containing pulses," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 14-37.
    10. Yutao Chen & Nazar Rozkvas & Mircea Lazar, 2020. "Driving Mode Optimization for Hybrid Trucks Using Road and Traffic Preview Data," Energies, MDPI, vol. 13(20), pages 1-18, October.
    11. Banaja Mohanty & Rajvikram Madurai Elavarasan & Hany M. Hasanien & Elangovan Devaraj & Rania A. Turky & Rishi Pugazhendhi, 2022. "Parameters Identification of Proton Exchange Membrane Fuel Cell Model Based on the Lightning Search Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, October.
    12. Bizon, Nicu, 2019. "Fuel saving strategy using real-time switching of the fueling regulators in the proton exchange membrane fuel cell system," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    13. Alessandro Serpi & Mario Porru, 2019. "Modelling and Design of Real-Time Energy Management Systems for Fuel Cell/Battery Electric Vehicles," Energies, MDPI, vol. 12(22), pages 1-21, November.
    14. Frangopoulos, Christos A., 2018. "Recent developments and trends in optimization of energy systems," Energy, Elsevier, vol. 164(C), pages 1011-1020.
    15. Nurdin, Hendra I. & Benmouna, Amel & Zhu, Bin & Chen, Jiayin & Becherif, Mohamed & Hissel, Daniel & Fletcher, John, 2024. "Maximum efficiency points of a proton-exchange membrane fuel cell system: Theory and experiments," Applied Energy, Elsevier, vol. 359(C).
    16. Bizon, Nicu & Thounthong, Phatiphat, 2018. "Real-time strategies to optimize the fueling of the fuel cell hybrid power source: A review of issues, challenges and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1089-1102.
    17. Ahmed M. Ali & Dirk Söffker, 2018. "Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions," Energies, MDPI, vol. 11(3), pages 1-24, February.
    18. Bizon, Nicu, 2018. "Effective mitigation of the load pulses by controlling the battery/SMES hybrid energy storage system," Applied Energy, Elsevier, vol. 229(C), pages 459-473.
    19. Mohsen Kandidayeni & Alvaro Macias & Loïc Boulon & João Pedro F. Trovão, 2020. "Online Modeling of a Fuel Cell System for an Energy Management Strategy Design," Energies, MDPI, vol. 13(14), pages 1-17, July.
    20. Nicu Bizon & Valentin Alexandru Stan & Angel Ciprian Cormos, 2019. "Optimization of the Fuel Cell Renewable Hybrid Power System Using the Control Mode of the Required Load Power on the DC Bus," Energies, MDPI, vol. 12(10), pages 1-15, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:139:y:2019:i:c:p:147-160. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.