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Use of metamodeling optimal approach promotes the performance of proton exchange membrane fuel cell (PEMFC)

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  • Cheng, Shan-Jen
  • Miao, Jr-Ming
  • Wu, Sheng-Ju

Abstract

The main purpose of this paper is to realize a metamodeling optimal approach that can be employed cost-efficiently and systematically to improve the performance of power density in PEMFC. First, an power density database is generated that corresponds to different levels of PEMFC unit operating parameters (factors) using the Design of Experiment (DoE) scheme, screening experiments, and Taguchi Orthogonal Array (OA). Then, metamodel is constructed by Radial Basis Function Neural Network (RBFNN) to represent the PEMFC system as a nonlinear complex model. The cross-validation procedure is implemented to prove the metamodel correctness and generalization. Moreover, Genetic Algorithm (GA) is applied to avoid local point and reduce time consumption to search the global optimum in promoting the performance of design factors. The proposed optimization methodology from experimental results provides an effective and economical approach to improve the performance of fuel cell unit and can be easy extended to the fuel cell stack system in energy applications.

Suggested Citation

  • Cheng, Shan-Jen & Miao, Jr-Ming & Wu, Sheng-Ju, 2013. "Use of metamodeling optimal approach promotes the performance of proton exchange membrane fuel cell (PEMFC)," Applied Energy, Elsevier, vol. 105(C), pages 161-169.
    • Handle: RePEc:eee:appene:v:105:y:2013:i:c:p:161-169
    DOI: 10.1016/j.apenergy.2013.01.001
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    1. Chiu, Han-Chieh & Jang, Jer-Huan & Yan, Wei-Mon & Li, Hung-Yi & Liao, Chih-Cheng, 2012. "A three-dimensional modeling of transport phenomena of proton exchange membrane fuel cells with various flow fields," Applied Energy, Elsevier, vol. 96(C), pages 359-370.
    2. Cheng, Shan-Jen & Miao, Jr-Ming & Wu, Sheng-Ju, 2012. "Investigating the effects of operational factors on PEMFC performance based on CFD simulations using a three-level full-factorial design," Renewable Energy, Elsevier, vol. 39(1), pages 250-260.
    3. Wang, Yun & Chen, Ken S. & Mishler, Jeffrey & Cho, Sung Chan & Adroher, Xavier Cordobes, 2011. "A review of polymer electrolyte membrane fuel cells: Technology, applications, and needs on fundamental research," Applied Energy, Elsevier, vol. 88(4), pages 981-1007, April.
    4. Tang, Yong & Yuan, Wei & Pan, Minqiang & Wan, Zhenping, 2011. "Experimental investigation on the dynamic performance of a hybrid PEM fuel cell/battery system for lightweight electric vehicle application," Applied Energy, Elsevier, vol. 88(1), pages 68-76, January.
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    Cited by:

    1. Puja Bhatt & Neha Agarwal & Uday K. Chakraborty, 2016. "Parameter Optimization of PEMFC with Genetic Algorithm," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 241-249, November.
    2. Kwan, Trevor Hocksun & Wu, Xiaofeng & Yao, Qinghe, 2018. "Multi-objective genetic optimization of the thermoelectric system for thermal management of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 217(C), pages 314-327.
    3. Besseris, George J., 2014. "Using qualimetric engineering and extremal analysis to optimize a proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 128(C), pages 15-26.
    4. Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "Machine learning methods to assist energy system optimization," Applied Energy, Elsevier, vol. 243(C), pages 191-205.
    5. Geyer, Philipp & Schlüter, Arno, 2014. "Automated metamodel generation for Design Space Exploration and decision-making – A novel method supporting performance-oriented building design and retrofitting," Applied Energy, Elsevier, vol. 119(C), pages 537-556.
    6. Lin, Chen & Yan, Xiaohui & Wei, Guanghua & Ke, Changchun & Shen, Shuiyun & Zhang, Junliang, 2019. "Optimization of configurations and cathode operating parameters on liquid-cooled proton exchange membrane fuel cell stacks by orthogonal method," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Kheirandish, Azadeh & Motlagh, Farid & Shafiabady, Niusha & Dahari, Mahidzal & Khairi Abdul Wahab, Ahmad, 2017. "Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system," Applied Energy, Elsevier, vol. 202(C), pages 20-31.
    8. Cho, Junhyun & Park, Jaeman & Oh, Hwanyeong & Min, Kyoungdoug & Lee, Eunsook & Jyoung, Jy-Young, 2013. "Analysis of the transient response and durability characteristics of a proton exchange membrane fuel cell with different micro-porous layer penetration thicknesses," Applied Energy, Elsevier, vol. 111(C), pages 300-309.
    9. Chen, Huicui & Pei, Pucheng & Song, Mancun, 2015. "Lifetime prediction and the economic lifetime of Proton Exchange Membrane fuel cells," Applied Energy, Elsevier, vol. 142(C), pages 154-163.
    10. Yang, Woo-Joo & Wang, Hong-Yang & Lee, Dae-Hyung & Kim, Young-Bae, 2015. "Channel geometry optimization of a polymer electrolyte membrane fuel cell using genetic algorithm," Applied Energy, Elsevier, vol. 146(C), pages 1-10.
    11. Pei, Pucheng & Chen, Huicui, 2014. "Main factors affecting the lifetime of Proton Exchange Membrane fuel cells in vehicle applications: A review," Applied Energy, Elsevier, vol. 125(C), pages 60-75.
    12. Xing, Lei & Das, Prodip K. & Song, Xueguan & Mamlouk, Mohamed & Scott, Keith, 2015. "Numerical analysis of the optimum membrane/ionomer water content of PEMFCs: The interaction of Nafion® ionomer content and cathode relative humidity," Applied Energy, Elsevier, vol. 138(C), pages 242-257.

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