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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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    1. Benaggoune, Khaled & Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine, 2022. "A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 313(C).
    2. Zhou, Daming & Gao, Fei & Breaz, Elena & Ravey, Alexandre & Miraoui, Abdellatif, 2017. "Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach," Energy, Elsevier, vol. 138(C), pages 1175-1186.
    3. Chen, Dongfang & Pei, Pucheng & Meng, Yining & Ren, Peng & Li, Yuehua & Wang, Mingkai & Wang, Xizhong, 2022. "Novel extraction method of working condition spectrum for the lifetime prediction and energy management strategy evaluation of automotive fuel cells," Energy, Elsevier, vol. 255(C).
    4. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
    5. Li, Bing & Wan, Kechuang & Xie, Meng & Chu, Tiankuo & Wang, Xiaolei & Li, Xiang & Yang, Daijun & Ming, Pingwen & Zhang, Cunman, 2022. "Durability degradation mechanism and consistency analysis for proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 314(C).
    6. Chu, Tiankuo & Wang, Qinpu & Xie, Meng & Wang, Baoyun & Yang, Daijun & Li, Bing & Ming, Pingwen & Zhang, Cunman, 2022. "Investigation of the reversible performance degradation mechanism of the PEMFC stack during long-term durability test," Energy, Elsevier, vol. 258(C).
    7. Shuo Sun & Junzhong Sun & Zongliang Wang & Zhiyong Zhou & Wei Cai, 2022. "Prediction of Battery SOH by CNN-BiLSTM Network Fused with Attention Mechanism," Energies, MDPI, vol. 15(12), pages 1-17, June.
    8. Pei, Pucheng & Chen, Dongfang & Wu, Ziyao & Ren, Peng, 2019. "Nonlinear methods for evaluating and online predicting the lifetime of fuel cells," Applied Energy, Elsevier, vol. 254(C).
    9. Zhang, Zhendong & Wang, Ya-Xiong & He, Hongwen & Sun, Fengchun, 2021. "A short- and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 304(C).
    10. Chu, Tiankuo & Zhang, Ruofan & Wang, Yanbo & Ou, Mingyang & Xie, Meng & Shao, Hangyu & Yang, Daijun & Li, Bing & Ming, Pingwen & Zhang, Cunman, 2021. "Performance degradation and process engineering of the 10 kW proton exchange membrane fuel cell stack," Energy, Elsevier, vol. 219(C).
    11. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    12. Pei, Pucheng & Meng, Yining & Chen, Dongfang & Ren, Peng & Wang, Mingkai & Wang, Xizhong, 2023. "Lifetime prediction method of proton exchange membrane fuel cells based on current degradation law," Energy, Elsevier, vol. 265(C).
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    2. 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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