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A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression

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  • Özçelep, Yasin
  • Sevgen, Selcuk
  • Samli, Ruya

Abstract

This paper presents an experimental study about the proton exchange membrane fuel cell (PEMFC) behavior on linearly increasing loads. The study mainly based on the effect of the linear load slope on hydrogen consumption for 0–600 W range and 0–100 Watt/s slope. Experimental results are processed by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR). The relationship between total consumed energy, peak power, slope and hydrogen consumption are discussed and novel equations are presented. The average error rates of ANN and MLR are 0.3189%, and 0.1124% while the average R2 values are 0.9965 for ANN simulation and 0.9545 MLR simulation. We presented that the energy and exergy efficiency are decreased 6%, cost of the energy is increased 13% with the increasing slope of the power. We also performed the sensitivity and uncertainty analysis. The results give information to hydrogen system designers about an effective way to reach hydrogen consumption by performing both of the modelling processes successfully.

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  • Özçelep, Yasin & Sevgen, Selcuk & Samli, Ruya, 2020. "A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression," Renewable Energy, Elsevier, vol. 156(C), pages 570-578.
    • Handle: RePEc:eee:renene:v:156:y:2020:i:c:p:570-578
    DOI: 10.1016/j.renene.2020.04.085
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    Cited by:

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    2. Jiao, Jieran & Chen, Fengxiang, 2022. "Humidity estimation of vehicle proton exchange membrane fuel cell under variable operating temperature based on adaptive sliding mode observation," Applied Energy, Elsevier, vol. 313(C).
    3. Jinrong Yang & Yichun Wu & Xingyang Liu, 2023. "Proton Exchange Membrane Fuel Cell Power Prediction Based on Ridge Regression and Convolutional Neural Network Data-Driven Model," Sustainability, MDPI, vol. 15(14), pages 1-31, July.
    4. Wu, Wei & Zhai, Chong & Sui, Zengguang & Sui, Yunren & Luo, Xianglong, 2021. "Proton exchange membrane fuel cell integrated with microchannel membrane-based absorption cooling for hydrogen vehicles," Renewable Energy, Elsevier, vol. 178(C), pages 560-573.
    5. Won, Jinyeon & Oh, Hwanyeong & Hong, Jongsup & Kim, Minjin & Lee, Won-Yong & Choi, Yoon-Young & Han, Soo-Bin, 2021. "Hybrid diagnosis method for initial faults of air supply systems in proton exchange membrane fuel cells," Renewable Energy, Elsevier, vol. 180(C), pages 343-352.
    6. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2021. "Prognosis of fuel cell degradation under different applications using wavelet analysis and nonlinear autoregressive exogenous neural network," Renewable Energy, Elsevier, vol. 179(C), pages 802-814.
    7. Deng, Shutong & Zhang, Jun & Zhang, Caizhi & Luo, Mengzhu & Ni, Meng & Li, Yu & Zeng, Tao, 2022. "Prediction and optimization of gas distribution quality for high-temperature PEMFC based on data-driven surrogate model," Applied Energy, Elsevier, vol. 327(C).

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