Abstract
To address the difficulty in obtaining fault data and improve the accuracy of fault diagnosis in the operation of proton exchange membrane fuel cells (PEMFC), this paper proposes a mechanism modeling method based on operational data. This method corrects the mechanism model of the proton exchange membrane fuel cell stack and auxiliary systems by actual operational data and uses it to simulate faults under given working conditions to obtain sample data. In addition, an improved GA-BP neural network algorithm is designed for the fault diagnosis system, which serially trains and tests the simulated fault data. Simulation results show that compared with the traditional BP neural network algorithm, the improved GA-BP neural network algorithm designed in this paper increases the minimum diagnostic accuracy of a single fault to above 93.5% and improves the average diagnostic accuracy by about 4.5%. This research method has important engineering application value.
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Acknowledgments
This work is supported by the National key research and development program, Fuel cell stack with High performance membrane electrode and ultra-thin titanium electrode plate under Grant 2022YFB2502401.
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Chen, H., Luo, W., Pan, D., Zhu, S., Chen, P., Li, C. (2024). Design of PEMFC Stack Intelligent Diagnosis System Based on Improved Neural Network. In: Sun, H., Pei, W., Dong, Y., Yu, H., You, S. (eds) Proceedings of the 10th Hydrogen Technology Convention, Volume 3. WHTC 2023. Springer Proceedings in Physics, vol 395. Springer, Singapore. https://doi.org/10.1007/978-981-99-8581-4_7
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DOI: https://doi.org/10.1007/978-981-99-8581-4_7
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