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A Data-Driven Prediction Method for Proton Exchange Membrane Fuel Cell Degradation

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  • Dan Wang

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
    Xiangyang Daan Automobile Test Center, Xiangyang 441148, China)

  • Haitao Min

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Honghui Zhao

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
    China FAW Corporation Limited, Changchun 130013, China)

  • Weiyi Sun

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Bin Zeng

    (Xiangyang Daan Automobile Test Center, Xiangyang 441148, China)

  • Qun Ma

    (Xiangyang Daan Automobile Test Center, Xiangyang 441148, China)

Abstract

This paper proposes a long short-term memory (LSTM) network to predict the power degradation of proton exchange membrane fuel cells (PEMFCs), and in order to promote the performance of the LSTM network, the ant colony algorithm (ACO) is introduced to optimize the hyperparameters of the LSTM network. First, the degradation mechanism of PEMFCs is analyzed. Second, the ACO algorithm is used to set the learning rate and dropout probability of the LSTM network combined with partial aging data, which can show the characteristics of the dataset. After that, the aging prediction model is built by using the LSTM and ACO (ACO-LSTM) method. Moreover, the convergence of the method is verified with previous studies. Finally, the fuel cell aging data provided by the Xiangyang Da’an Automotive Testing Center are used for verification. The results show that, compared with the traditional LSTM network, ACO-LSTM can predict the aging process of PEMFCs more accurately, and its prediction accuracy is improved by about 35%, especially when the training data are less. At the same time, the performance of the model trained by ACO-LSTM is also excellent under other operating conditions of the same fuel cell, and it has strong versatility.

Suggested Citation

  • Dan Wang & Haitao Min & Honghui Zhao & Weiyi Sun & Bin Zeng & Qun Ma, 2024. "A Data-Driven Prediction Method for Proton Exchange Membrane Fuel Cell Degradation," Energies, MDPI, vol. 17(4), pages 1-17, February.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:968-:d:1341420
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    References listed on IDEAS

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