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Performance degradation prediction method of PEM fuel cells using bidirectional long short-term memory neural network based on Bayesian optimization

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  • Chen, Dongfang
  • Wu, Wenlong
  • Chang, Kuanyu
  • Li, Yuehua
  • Pei, Pucheng
  • Xu, Xiaoming

Abstract

Proton exchange membrane (PEM) fuel cell is the core equipment that can directly convert hydrogen energy into electricity. In the process of long-term operation, due to the aging of membrane electrode assembly and other components, the fuel cell performance gradually deteriorates. The voltage prediction of fuel cells is very important for performance and lifetime optimization. Long short-term memory neural network is one of the widely used prediction methods. Based on the prediction method of bidirectional long short-term memory neural network, the hyperparameters of the neural network model by Bayesian optimization algorithm is optimized to improve the accuracy of fuel cell performance degradation prediction. When the sampling time interval is 25 min and the training set is 45 %, the root mean square error and the average absolute percentage error of the prediction results is reduced to 6.3 mV and 0.1245 %, respectively. Moreover, by analyzing the influence of different sampling time intervals and training set proportion on the prediction results, a data set that takes into accounts both efficiency and accuracy is obtained. The proposed method based on Bayesian optimization can achieve more accurate performance degradation prediction.

Suggested Citation

  • Chen, Dongfang & Wu, Wenlong & Chang, Kuanyu & Li, Yuehua & Pei, Pucheng & Xu, Xiaoming, 2023. "Performance degradation prediction method of PEM fuel cells using bidirectional long short-term memory neural network based on Bayesian optimization," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223028633
    DOI: 10.1016/j.energy.2023.129469
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    Cited by:

    1. Lingling Lv & Pucheng Pei & Peng Ren & He Wang & Geng Wang, 2025. "Exploring Performance Degradation of Proton Exchange Membrane Fuel Cells Based on Diffusion Transformer Model," Energies, MDPI, vol. 18(5), pages 1-22, February.
    2. Huang, Ruike & Zhang, Xuexia & Dong, Sidi & Huang, Lei & Liao, Hongbo & Li, Yuan, 2024. "A refined grey Verhulst model for accurate degradation prognostication of PEM fuel cells based on inverse hyperbolic sine function transformation," Renewable Energy, Elsevier, vol. 237(PC).
    3. Chen, Li & Yang, Jibin & Wu, Xiaohua & Deng, Pengyi & Xu, Xiaohui & Peng, Yiqiang, 2025. "Remaining useful life prediction of PEMFCs based on mode decomposition and hybrid method under real-world traffic conditions," Energy, Elsevier, vol. 314(C).
    4. Yuan, Hong & Ma, Xin & Ma, Minda & Ma, Juan, 2024. "Hybrid framework combining grey system model with Gaussian process and STL for CO2 emissions forecasting in developed countries," Applied Energy, Elsevier, vol. 360(C).
    5. Xiao, Xiao & Zhang, Xuan & Song, Meiqi & Liu, Xiaojing & Huang, Qingyu, 2024. "NPP accident prevention: Integrated neural network for coupled multivariate time series prediction based on PSO and its application under uncertainty analysis for NPP data," Energy, Elsevier, vol. 305(C).
    6. Motalleb Miri & Ivan Tolj & Frano Barbir, 2024. "Review of Proton Exchange Membrane Fuel Cell-Powered Systems for Stationary Applications Using Renewable Energy Sources," Energies, MDPI, vol. 17(15), pages 1-26, August.
    7. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).

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