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Multiple Model Predictive Hybrid Feedforward Control of Fuel Cell Power Generation System

Author

Listed:
  • Long Wu

    (Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Sipailou 2, Nanjing 210096, China)

  • Li Sun

    (Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Sipailou 2, Nanjing 210096, China)

  • Jiong Shen

    (Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Sipailou 2, Nanjing 210096, China)

  • Qingsong Hua

    (School of Mechanical and Electrical Engineering, Qingdao University, Ningxia Road 308, Qingdao 266071, China)

Abstract

Solid oxide fuel cell (SOFC) is widely considered as an alternative solution among the family of the sustainable distributed generation. Its load flexibility enables it adjusting the power output to meet the requirements from power grid balance. Although promising, its control is challenging when faced with load changes, during which the output voltage is required to be maintained as constant and fuel utilization rate kept within a safe range. Moreover, it makes the control even more intractable because of the multivariable coupling and strong nonlinearity within the wide-range operating conditions. To this end, this paper developed a multiple model predictive control strategy for reliable SOFC operation. The resistance load is regarded as a measurable disturbance, which is an input to the model predictive control as feedforward compensation. The coupling is accommodated by the receding horizon optimization. The nonlinearity is mitigated by the multiple linear models, the weighted sum of which serves as the final control execution. The merits of the proposed control structure are demonstrated by the simulation results.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:2:p:437-:d:130733
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    References listed on IDEAS

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    Cited by:

    1. Jie Ma & Suning Ma & Xinyi Zhang & Daifen Chen & Juan He, 2018. "Development of Large-Scale and Quasi Multi-Physics Model for Whole Structure of the Typical Solid Oxide Fuel Cell Stacks," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    2. 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.

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