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Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack

Author

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  • Doudou N. Luta

    (Department of Electrical Electronic and Computer Engineering, Cape Peninsula University of Technology, P.O. Box 1906, Bellville Cape Town 7535, South Africa)

  • Atanda K. Raji

    (Department of Electrical Electronic and Computer Engineering, Cape Peninsula University of Technology, P.O. Box 1906, Bellville Cape Town 7535, South Africa)

Abstract

The negative environmental impact and the rapidly declining reserve of fossil fuel-based energy sources for electricity generation is a big challenge to finding sustainable alternatives. This scenario is complicated by the ever-increasing world population growth demanding a higher standard of living. A fuel cell system is able to generate electricity and water with higher energy efficiency while producing near-zero emissions. A common fuel cell stack displays a nonlinear power characteristic as a result of internal limitations and operating parameters such as temperature, hydrogen and oxygen partial pressures and humidity levels, leading to a reduced overall system performance. It is therefore important to extract as much power as possible from the stack, thus hindering excessive fuel use. This study considers and compares two Maximum Power Point Tracking (MPPT) approaches; one based on the Mamdani Fuzzy Inference System and the other on the Particle Swarm Optimisation (PSO) algorithm to maintain the output power of a fuel cell stack extremely close to its maximum. To ensure that, the power converter interfaced to the fuel cell unit must be able to continuously self-modify its parameters, hence changing its voltage and current depending upon the Maximum Power Point position. While various methods exist for Maximum Power Point tracker design, this paper analyses the response characteristics of a Mamdani Fuzzy Inference Engine and the Particle Swarm Optimisation technique. The investigation was conducted on a 53 kW Proton Exchange Membrane Fuel Cell interfaced to a DC-to-DC boost converter supplying 1.2 kV from a 625 V input DC voltage. The modelling was accomplished using a Matlab/Simulink environment. The results showed that the MPPT controller based on the PSO algorithm presented better tracking efficiency as compared to the Mamdani controller. Furthermore, the rise time of the PSO controller was slightly shorter than the Mamdani controller and the overshoot of the PSO controller was 2% lower than that of the Mamdani controller.

Suggested Citation

  • Doudou N. Luta & Atanda K. Raji, 2019. "Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack," Energies, MDPI, vol. 12(5), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:936-:d:212737
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    References listed on IDEAS

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    Cited by:

    1. Hegazy Rezk & Ahmed Fathy, 2020. "Performance Improvement of PEM Fuel Cell Using Variable Step-Size Incremental Resistance MPPT Technique," Sustainability, MDPI, vol. 12(14), pages 1-16, July.
    2. Nicu Bizon & Mircea Raceanu & Emmanouel Koudoumas & Adriana Marinoiu & Emmanuel Karapidakis & Elena Carcadea, 2020. "Renewable/Fuel Cell Hybrid Power System Operation Using Two Search Controllers of the Optimal Power Needed on the DC Bus," Energies, MDPI, vol. 13(22), pages 1-26, November.
    3. Mohammed Yousri Silaa & Mohamed Derbeli & Oscar Barambones & Ali Cheknane, 2020. "Design and Implementation of High Order Sliding Mode Control for PEMFC Power System," Energies, MDPI, vol. 13(17), pages 1-15, August.
    4. Muhammad Majid Gulzar, 2023. "Maximum Power Point Tracking of a Grid Connected PV Based Fuel Cell System Using Optimal Control Technique," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    5. Mokhtar Aly & Emad A. Mohamed & Hegazy Rezk & Ahmed M. Nassef & Mostafa A. Elhosseini & Ahmed Shawky, 2023. "An Improved Optimally Designed Fuzzy Logic-Based MPPT Method for Maximizing Energy Extraction of PEMFC in Green Buildings," Energies, MDPI, vol. 16(3), pages 1-23, January.
    6. Mohamed Derbeli & Oscar Barambones & Jose Antonio Ramos-Hernanz & Lassaad Sbita, 2019. "Real-Time Implementation of a Super Twisting Algorithm for PEM Fuel Cell Power System," Energies, MDPI, vol. 12(9), pages 1-20, April.
    7. Khlid Ben Hamad & Doudou N. Luta & Atanda K. Raji, 2021. "A Grid-Tied Fuel Cell Multilevel Inverter with Low Harmonic Distortions," Energies, MDPI, vol. 14(3), pages 1-24, January.
    8. Mohamed Derbeli & Asma Charaabi & Oscar Barambones & Cristian Napole, 2021. "High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    9. Rezk, Hegazy & Aly, Mokhtar & Fathy, Ahmed, 2021. "A novel strategy based on recent equilibrium optimizer to enhance the performance of PEM fuel cell system through optimized fuzzy logic MPPT," Energy, Elsevier, vol. 234(C).
    10. Bonan Huang & Chaoming Zheng & Qiuye Sun & Ruixue Hu, 2019. "Optimal Economic Dispatch for Integrated Power and Heating Systems Considering Transmission Losses," Energies, MDPI, vol. 12(13), pages 1-19, June.

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