IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v322y2025ics0360544225012186.html

Design and experimental validation for performance analysis of non-isolated power converter topologies in fuel cell integrated dynamic load based local energy systems

Author

Listed:
  • Mounika, Kandi
  • Bhattacharjee, Ankur

Abstract

This paper presents performance analysis of different compact non-isolated DC-DC power electronic converters in a fuel cell-dynamic load-integrated local energy system. The first phase of work includes regulating input flow rate for fuel cells to achieve improved efficiency. The second phase includes integrated system design, operation, and control to reduce total harmonic distortion (THD) of power converters and improve power conversion efficiency. MATLAB/Simulink model, a 1.26 kW, 24V proton exchange membrane fuel cell (PEMFC) integrated with DC-DC buck converter, boost converter, buck-boost converter, sepic converter, cuk converter, single phase inverter and dynamic load profile are considered. DC-DC boost converter is found as the most suitable compared to other power converters in terms of speed of response, power quality (%THD), and efficiency. Finally, performance of the DC-DC boost converter is validated by experimental results of a practical PEMFC stack integrated dynamic load management system. Boost converter achieves efficiencies of 98.57 % and 97.6 % for R and RL loads, respectively, and maintains good power quality with %THD levels of 0.58 % and 1.36 % at the inverter side. The connected single-phase off-grid inverter achieves efficiency of 97.36 % under RL load. The outcome provides an efficient energy conversion solution for waste-to-electricity and off-grid local community demand-side management.

Suggested Citation

  • Mounika, Kandi & Bhattacharjee, Ankur, 2025. "Design and experimental validation for performance analysis of non-isolated power converter topologies in fuel cell integrated dynamic load based local energy systems," Energy, Elsevier, vol. 322(C).
  • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012186
    DOI: 10.1016/j.energy.2025.135576
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225012186
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.135576?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Zheng, Weiguang & Ma, Mengcheng & Xu, Enyong & Huang, Qibai, 2024. "An energy management strategy for fuel-cell hybrid electric vehicles based on model predictive control with a variable time domain," Energy, Elsevier, vol. 312(C).
    2. Soltanian, Salman & Kalogirou, Soteris A. & Ranjbari, Meisam & Amiri, Hamid & Mahian, Omid & Khoshnevisan, Benyamin & Jafary, Tahereh & Nizami, Abdul-Sattar & Gupta, Vijai Kumar & Aghaei, Siavash & Pe, 2022. "Exergetic sustainability analysis of municipal solid waste treatment systems: A systematic critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. Wang, Renkang & Li, Kai & Ming, Yuan & Guo, Wenjun & Deng, Bo & Tang, Hao, 2024. "An enhanced salp swarm algorithm with chaotic mapping and dynamic learning for optimizing purge process of proton exchange membrane fuel cell systems," Energy, Elsevier, vol. 308(C).
    4. Bicer, Y. & Dincer, I. & Aydin, M., 2016. "Maximizing performance of fuel cell using artificial neural network approach for smart grid applications," Energy, Elsevier, vol. 116(P1), pages 1205-1217.
    5. Xiong, Zhe & Yuan, Yupeng & Tong, Liang & Li, Xiao & Shen, Boyang, 2024. "Dynamic performance analysis of proton exchange membrane fuel cell in marine applications," Energy, Elsevier, vol. 310(C).
    6. Hessami, Mir-Akbar & Christensen, Sky & Gani, Robert, 1996. "Anaerobic digestion of household organic waste to produce biogas," Renewable Energy, Elsevier, vol. 9(1), pages 954-957.
    7. Tang, Tianfeng & Peng, Qianlong & Shi, Qing & Peng, Qingguo & Zhao, Jin & Chen, Chaoyi & Wang, Guangwei, 2024. "Energy management of fuel cell hybrid electric bus in mountainous regions: A deep reinforcement learning approach considering terrain characteristics," Energy, Elsevier, vol. 311(C).
    8. Zhao, Yinghua & Huang, Siqi & Wang, Xiaoyu & Shi, Jingwu & Yao, Shouwen, 2024. "Energy management with adaptive moving average filter and deep deterministic policy gradient reinforcement learning for fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 312(C).
    9. Wang, Siyu & Yang, Duo & Yan, Fuhui & Yu, Kunjie, 2024. "Comparison of deep reinforcement learning-based energy management strategies for fuel cell vehicles considering economics, durability and adaptability," Energy, Elsevier, vol. 307(C).
    10. Abdin, Z. & Webb, C.J. & Gray, E.MacA., 2016. "PEM fuel cell model and simulation in Matlab–Simulink based on physical parameters," Energy, Elsevier, vol. 116(P1), pages 1131-1144.
    Full references (including those not matched with items on IDEAS)

    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. Yang, Duo & Pan, Rui & Wang, Yujie & Chen, Zonghai, 2019. "Modeling and control of PEMFC air supply system based on T-S fuzzy theory and predictive control," Energy, Elsevier, vol. 188(C).
    2. Zheng Huang & Laisuo Su & Yunjie Yang & Linsong Gao & Xinyu Liu & Heng Huang & Yubai Li & Yongchen Song, 2023. "Three-Dimensional Simulation on the Effects of Different Parameters and Pt Loading on the Long-Term Performance of Proton Exchange Membrane Fuel Cells," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    3. Igourzal, Ayoub & Auger, François & Olivier, Jean-Christophe & Retière, Clément, 2024. "Electrical, thermal and degradation modelling of PEMFCs for naval applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PA), pages 34-49.
    4. Elena C. Prenovitz & Peter K. Hazlett & Chandler S. Reilly, 2023. "Can Markets Improve Recycling Performance? A Cross-Country Regression Analysis and Case Studies," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    5. Xu, Liangfei & Fang, Chuan & Li, Jianqiu & Ouyang, Minggao & Lehnert, Werner, 2018. "Nonlinear dynamic mechanism modeling of a polymer electrolyte membrane fuel cell with dead-ended anode considering mass transport and actuator properties," Applied Energy, Elsevier, vol. 230(C), pages 106-121.
    6. Yuan, Yi & Chen, Li & Lyu, Xingbao & Ning, Wenjing & Liu, Wenqi & Tao, Wen-Quan, 2024. "Modeling and optimization of a residential PEMFC-based CHP system under different operating modes," Applied Energy, Elsevier, vol. 353(PA).
    7. Li, Bin & Wu, Zhangxi & Li, Ye & He, Jiawei & Wang, Bowen & Jiao, Kui & Hu, Xinhang & Fan, Hui & Wu, Jianzhong, 2025. "Thermal-water-electrical coupling modeling of PEMFC and its dynamic performance analysis under different operating conditions," Applied Energy, Elsevier, vol. 398(C).
    8. Costantini, Michele & Provolo, Giorgio & Bacenetti, Jacopo, 2024. "The effects of incorporating renewable energy into the environmental footprint of beef production," Energy, Elsevier, vol. 289(C).
    9. Cai, Shanshan & Yang, Juncheng & Zou, Yuqi & Hua, Zhipeng & Li, Song & Tu, Zhengkai, 2025. "Energy-exergy-emergy optimization analysis designed for combined cooling and power systems driven by proton-exchange membrane fuel cell," Energy, Elsevier, vol. 329(C).
    10. Zhang, Luyao & Wang, Xueke & Abed, Azher M. & Yin, Hengbin & Abdullaev, Sherzod & Fouad, Yasser & Dahari, Mahidzal & Mahariq, Ibrahim, 2024. "Economic/sustainability optimization/analysis of an environmentally friendly trigeneration biomass gasification system using advanced machine learning," Energy, Elsevier, vol. 308(C).
    11. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
    12. Iqbal, Najam & He, Guanzhang & Wang, Hu & Lin, Zhiqiang & Zheng, Zunqing & Yao, Mingfa, 2025. "Holistic energy management strategy for hybrid electric heavy-duty vehicles based on proximal policy optimization with the consideration of cabin temperature comfort," Energy, Elsevier, vol. 326(C).
    13. Xu, Shuhui & Wang, Yong & Wang, Zhi, 2019. "Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method," Energy, Elsevier, vol. 173(C), pages 457-467.
    14. Trung-Huong Tran & Karthik Kannan & Amornchai Arpornwichanop & Yong-Song Chen, 2025. "Enhancing the Energy Efficiency of a Proton Exchange Membrane Fuel Cell with a Dead-Ended Anode Using a Buffer Tank," Energies, MDPI, vol. 18(13), pages 1-14, June.
    15. Arsad, A.Z. & Hannan, M.A. & Ong, H.C. & Ker, Pin Jern & Wong, Richard TK. & Begum, R.A. & Jang, Gilsoo & Mahlia, T M Indra, 2025. "Artificial intelligence in hydrogen energy transitions: A comprehensive survey and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
    16. Huang, Ruchen & He, Hongwen & Su, Qicong & Wu, Jingda, 2025. "Towards sustainable and intelligent urban transportation: A novel deep transfer reinforcement learning framework for eco-driving of fuel cell buses," Energy, Elsevier, vol. 330(C).
    17. Campuzano, Felipe & Agudelo, Andrés F. & Martínez, Juan Daniel & Roberts, William L., 2025. "On the exergoeconomics of the thermochemical recycling of end-of-life tires by pyrolysis," Energy, Elsevier, vol. 330(C).
    18. Mari-Isabella Stan, 2022. "An Analysis of the Municipal Waste Management of Romania and Bulgaria in the European Context," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 166-174, September.
    19. Zhao, Jian & Ozden, Adnan & Shahgaldi, Samaneh & Alaefour, Ibrahim E. & Li, Xianguo & Hamdullahpur, Feridun, 2018. "Effect of Pt loading and catalyst type on the pore structure of porous electrodes in polymer electrolyte membrane (PEM) fuel cells," Energy, Elsevier, vol. 150(C), pages 69-76.
    20. Zou, Wei & Froning, Dieter & Shi, Yan & Lehnert, Werner, 2021. "Working zone for a least-squares support vector machine for modeling polymer electrolyte fuel cell voltage," Applied Energy, Elsevier, vol. 283(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:energy:v:322:y:2025:i:c:s0360544225012186. 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/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.