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A sensor-less methanol concentration control system based on feedback from the stack temperature

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  • An, Myung-Gi
  • Mehmood, Asad
  • Ha, Heung Yong

Abstract

A sensor-less methanol concentration control system based on feedback from the stack temperature (SLCCF) has been developed. The SLCCF algorithm is embedded into an in-house LabVIEW program that has been developed to control the methanol concentration in the feed of direct methanol fuel cells (DMFCs). This control method utilizes the close correlation between the stack temperature and the methanol concentration in the feed. Basically, the amounts of methanol to be supplied to the re-circulating feed stream are determined by estimating the methanol consumption rates under given operating conditions, which are then adjusted by a proportional–integral controller and supplied into the feed stream to maintain the stack temperature at a set value. The algorithm is designed to control the methanol concentration and the stack temperature for both start-up and normal operation processes. Feasibility tests with a 200W-class DMFC system under various operating conditions confirm that the algorithm successfully maintains the methanol concentration in the feed as well as the stack temperature at set values, and the start-up time required for the DMFC system to reach steady-state operating conditions is reduced significantly compared with conventional sensor-less methods.

Suggested Citation

  • An, Myung-Gi & Mehmood, Asad & Ha, Heung Yong, 2014. "A sensor-less methanol concentration control system based on feedback from the stack temperature," Applied Energy, Elsevier, vol. 131(C), pages 257-266.
    • Handle: RePEc:eee:appene:v:131:y:2014:i:c:p:257-266
    DOI: 10.1016/j.apenergy.2014.06.017
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    References listed on IDEAS

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    1. Achmad, F. & Kamarudin, S.K. & Daud, W.R.W. & Majlan, E.H., 2011. "Passive direct methanol fuel cells for portable electronic devices," Applied Energy, Elsevier, vol. 88(5), pages 1681-1689, May.
    2. Kim, Joon-Hee & Yang, Min-Jee & Park, Jun-Young, 2014. "Improvement on performance and efficiency of direct methanol fuel cells using hydrocarbon-based membrane electrode assembly," Applied Energy, Elsevier, vol. 115(C), pages 95-102.
    3. Zainoodin, A.M. & Kamarudin, S.K. & Masdar, M.S. & Daud, W.R.W. & Mohamad, A.B. & Sahari, J., 2014. "High power direct methanol fuel cell with a porous carbon nanofiber anode layer," Applied Energy, Elsevier, vol. 113(C), pages 946-954.
    4. Kamaruddin, M.Z.F. & Kamarudin, S.K. & Daud, W.R.W. & Masdar, M.S., 2013. "An overview of fuel management in direct methanol fuel cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 557-565.
    5. Seo, Sang Hern & Lee, Chang Sik, 2010. "A study on the overall efficiency of direct methanol fuel cell by methanol crossover current," Applied Energy, Elsevier, vol. 87(8), pages 2597-2604, August.
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    Cited by:

    1. Liu, Guicheng & Li, Xinyang & Wang, Hui & Liu, Xiuying & Chen, Ming & Woo, Jae Young & Kim, Ji Young & Wang, Xindong & Lee, Joong Kee, 2017. "Design of 3-electrode system for in situ monitoring direct methanol fuel cells during long-time running test at high temperature," Applied Energy, Elsevier, vol. 197(C), pages 163-168.
    2. An, Myung-Gi & Mehmood, Asad & Hwang, Jinyeon & Ha, Heung Yong, 2016. "A novel method of methanol concentration control through feedback of the amplitudes of output voltage fluctuations for direct methanol fuel cells," Energy, Elsevier, vol. 100(C), pages 217-226.
    3. Chi, Xuncheng & Chen, Fengxiang & Mo, Tiande & Li, Yu & Wei, Wei, 2024. "Improve methanol efficiency for direct methanol fuel cell system via investigation and control of optimal operating methanol concentration," Energy, Elsevier, vol. 290(C).
    4. An, Myung-Gi & Mehmood, Asad & Ha, Heung Yong, 2014. "Sensor-less control of the methanol concentration of direct methanol fuel cells at varying ambient temperatures," Applied Energy, Elsevier, vol. 129(C), pages 104-111.
    5. Chen, Fengxiang & Chi, Xuncheng & Wei, Wei & Mo, Tiande & Li, Yu, 2023. "Model-based observer for direct methanol fuel cell concentration estimation by using second-order sliding-mode algorithm," Energy, Elsevier, vol. 263(PD).

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