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Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

Author

Listed:
  • Yuxiao Qin

    (Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China)

  • Guodong Zhao

    (School of Information Engineering, Ningxia University, Yinchuan 750021, China)

  • Qingsong Hua

    (College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875, China)

  • Li Sun

    (Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China)

  • Soumyadeep Nag

    (Department of Electrical & Computer Engineering, Baylor University, Waco, TX 76798, USA)

Abstract

Nowadays, given the great deal of fossil fuel consumption and associated environmental pollution, solid oxide fuel cells (SOFCs) have shown their great merits in terms of high energy conversion efficiency and low emissions as a stationary power source. To ensure power quality and efficiency, both the output voltage and fuel utilization of an SOFC should be tightly controlled. However, these two control objectives usually conflict with each other, making the controller design of an SOFC quite challenging and sophisticated. To this end, a multi-objective genetic algorithm (MOGA) was employed to tune the proportional–integral–derivative (PID) controller parameters through the following steps: (1) Identifying the SOFC system through a least squares method; (2) designing the control based on a relative gain array (RGA) analysis; and (3) applying the MOGA to a simulation to search for a set of optimal solutions. By comparing the control performance of the Pareto solutions, satisfactory control parameters were determined. The simulation results demonstrated that the proposed method could reduce the impact of disturbances and regulate output voltage and fuel utilization simultaneously (with strong robustness).

Suggested Citation

  • Yuxiao Qin & Guodong Zhao & Qingsong Hua & Li Sun & Soumyadeep Nag, 2019. "Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization," Sustainability, MDPI, vol. 11(12), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3290-:d:239867
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    References listed on IDEAS

    as
    1. Long Wu & Li Sun & Jiong Shen & Qingsong Hua, 2018. "Multiple Model Predictive Hybrid Feedforward Control of Fuel Cell Power Generation System," Sustainability, MDPI, vol. 10(2), pages 1-19, February.
    2. Li Sun & Qingsong Hua & Jiong Shen & Yali Xue & Donghai Li & Kwang Y. Lee, 2017. "A Combined Voltage Control Strategy for Fuel Cell," Sustainability, MDPI, vol. 9(9), pages 1-15, August.
    3. Yuxiao Qin & Li Sun & Qingsong Hua & Ping Liu, 2018. "A Fuzzy Adaptive PID Controller Design for Fuel Cell Power Plant," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    4. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    5. Sun, Li & Shen, Jiong & Hua, Qingsong & Lee, Kwang Y., 2018. "Data-driven oxygen excess ratio control for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 231(C), pages 866-875.
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    2. Husam A. Neamah & Mohammed Dulaimi & Alaa Silavinia & Aminu Babangida & Péter Tamás Szemes, 2024. "Development of a Volkswagen Jetta MK5 Hybrid Vehicle for Optimized System Efficiency Based on a Genetic Algorithm," Energies, MDPI, vol. 17(5), pages 1-27, February.

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