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Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm

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  • Sun, Xianke
  • Wang, Gaoliang
  • Xu, Liuyang
  • Yuan, Honglei
  • Yousefi, Nasser

Abstract

The present study proposes a new efficient methodology for optimal model identification of the Proton-exchange membrane fuel cell (PEMFC) stacks based on an improved version of a Deep Belief Network (DBN). The proposed DBN has been updated by a new metaheuristic to provide the minimum relative error between the experimental output voltage and the network output data during simulation of the nonlinear transient behavior of the Proton-exchange Membrane Fuel Cells (PEMFC). To develop the effectiveness of the DBN, an improved version of the Archimedes optimization algorithm (IAOA) has been developed. The results of training and testing of the proposed method are compared with the original DBN model to indicate the method's effectiveness. Simulations showed 34.0879 and 28.5016 V for the DBN and the suggested DBN-IAOA methods, respectively. This indicates the higher performance of the suggested method toward the original DBN model and its well-organization for modeling the PEMFC stacks.

Suggested Citation

  • Sun, Xianke & Wang, Gaoliang & Xu, Liuyang & Yuan, Honglei & Yousefi, Nasser, 2021. "Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm," Energy, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:energy:v:237:y:2021:i:c:s0360544221017801
    DOI: 10.1016/j.energy.2021.121532
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    Cited by:

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    2. Li, Naiqing & Li, Longhao & Zhang, Fan & Jiao, Ticao & Wang, Shuang & Liu, Xuefeng & Wu, Xinghua, 2023. "Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model," Energy, Elsevier, vol. 277(C).
    3. Abdel-Basset, Mohamed & Mohamed, Reda & Abouhawwash, Mohamed, 2023. "On the facile and accurate determination of the highly accurate recent methods to optimize the parameters of different fuel cells: Simulations and analysis," Energy, Elsevier, vol. 272(C).
    4. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    5. Hosseini Dehshiri, Seyyed Shahabaddin, 2022. "A new application of multi criteria decision making in energy technology in traditional buildings: A case study of Isfahan," Energy, Elsevier, vol. 240(C).
    6. Hegazy Rezk & Tabbi Wilberforce & A. G. Olabi & Rania M. Ghoniem & Enas Taha Sayed & Mohammad Ali Abdelkareem, 2023. "Optimal Parameter Identification of a PEM Fuel Cell Using Recent Optimization Algorithms," Energies, MDPI, vol. 16(14), pages 1-20, July.
    7. Liyuan Shao & Yong Zhang & Xiujuan Zheng & Xin He & Yufeng Zheng & Zhiwei Liu, 2023. "A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods," Energies, MDPI, vol. 16(3), pages 1-22, February.
    8. Fathy, Ahmed & Rezk, Hegazy & Alharbi, Abdullah G. & Yousri, Dalia, 2023. "Proton exchange membrane fuel cell model parameters identification using Chaotically based-bonobo optimizer," Energy, Elsevier, vol. 268(C).
    9. Hasanien, Hany M. & Shaheen, Mohamed A.M. & Turky, Rania A. & Qais, Mohammed H. & Alghuwainem, Saad & Kamel, Salah & Tostado-Véliz, Marcos & Jurado, Francisco, 2022. "Precise modeling of PEM fuel cell using a novel Enhanced Transient Search Optimization algorithm," Energy, Elsevier, vol. 247(C).
    10. Khajavi, Hamed & Rastgoo, Amir, 2023. "Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms," Energy, Elsevier, vol. 272(C).

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