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Design and implementation of model predictive control for an open-cathode fuel cell thermal management system

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  • Zhang, Bo
  • Lin, Fei
  • Zhang, Caizhi
  • Liao, Ruiyue
  • Wang, Ya-Xiong

Abstract

The aim of this study is to design a thermal management system to address the temperature regulation and fluctuation of an open-cathode proton exchange membrane (PEM) fuel cell. A model predictive control (MPC) approach is newly proposed to command of blowers in the fuel cell. First, a thermal management-oriented model of the fuel cell is set up, and linearized by using Taylor formula. Then, the closed-loop feedback MPC according to the linearized state-space model is developed. An experimental test rig via LabVIEW-based thermal control system prototype is configured. Finally, different load current tests were applied to verify the effectiveness, and the performance of the MPC controller was compared with a traditional PI controller. The results of this study demonstrate that the proposed MPC can effectively manipulate the stack temperature to track the reference trajectory under the constant current as well as dynamic load schedules. In particular, in a combined driving cycle equivalent test condition, the fuel cell temperature reached to the target after a settling time of 146.6 s, and temperature fluctuations were less than ±0.5 °C with the MAE value of 0.223 and the RMSE value of 0.346.

Suggested Citation

  • Zhang, Bo & Lin, Fei & Zhang, Caizhi & Liao, Ruiyue & Wang, Ya-Xiong, 2020. "Design and implementation of model predictive control for an open-cathode fuel cell thermal management system," Renewable Energy, Elsevier, vol. 154(C), pages 1014-1024.
    • Handle: RePEc:eee:renene:v:154:y:2020:i:c:p:1014-1024
    DOI: 10.1016/j.renene.2020.03.073
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    References listed on IDEAS

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    Cited by:

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    3. Kurnia, Jundika C. & Chaedir, Benitta A. & Sasmito, Agus P. & Shamim, Tariq, 2021. "Progress on open cathode proton exchange membrane fuel cell: Performance, designs, challenges and future directions," Applied Energy, Elsevier, vol. 283(C).
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    5. Gong, Chengyuan & Xing, Lu & Liang, Cong & Tu, Zhengkai, 2022. "Modeling and dynamic characteristic simulation of air-cooled proton exchange membrane fuel cell stack for unmanned aerial vehicle," Renewable Energy, Elsevier, vol. 188(C), pages 1094-1104.
    6. Zhang, Jun & Zhang, Caizhi & Li, Jin & Deng, Bo & Fan, Min & Ni, Meng & Mao, Zhanxin & Yuan, Honggeng, 2021. "Multi-perspective analysis of CO poisoning in high-temperature proton exchange membrane fuel cell stack via numerical investigation," Renewable Energy, Elsevier, vol. 180(C), pages 313-328.
    7. Min, Dehao & Song, Zhen & Chen, Huicui & Wang, Tianxiang & Zhang, Tong, 2022. "Genetic algorithm optimized neural network based fuel cell hybrid electric vehicle energy management strategy under start-stop condition," Applied Energy, Elsevier, vol. 306(PB).
    8. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells," Applied Energy, Elsevier, vol. 293(C).
    9. Ouyang, Tiancheng & Chen, Jingxian & Liu, Wenjun & Xu, Peihang & Lu, Jie & Zhao, Zhongkai, 2022. "A comprehensive evaluation for microfluidic fuel cells from anti-vibration viewpoint using phase field theory," Renewable Energy, Elsevier, vol. 189(C), pages 676-693.

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