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Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters

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  • Pang, Ran
  • Zhang, Caizhi
  • Dai, Haifeng
  • Bai, Yunfeng
  • Hao, Dong
  • Chen, Jinrui
  • Zhang, Bin

Abstract

In vehicular fuel cell, the change of operating parameters (pressure, temperature, humidity) may lead to health problem, which is a key parameter for fuel cell system shutdown. In this study, the health state of the proton exchange membrane fuel cell is recognized by considering several typical operating parameters. The cell voltage consistency (spatial fluctuation degree) is used to characterize the health state of fuel cell. Specifically, the health state of the minimum cell voltage is also considered. The process of health states labeling is achieved with the non-parametric statistics and unsupervised learning methods by calculating the threshold values for health evaluation indexes. Moreover, a variety of feature selection methods are applied to select the features which have relatively significant on health of fuel cell for improving the efficiency of health recognition. In addition, the random forest algorithm is used to identify the health state of based on the results of feature selection. The main results show that the relatively optimal features are temperature, current, cathode stoichiometry and pressure, respectively. Furthermore, the accuracy rate of random forest algorithm achieves to 95.04%. The effectiveness of the proposed methods is validated under operation condition of low current density and various temperatures by the results of dynamic loading experiments. The presented method of health recognition can be used to health management of fuel cell vehicle.

Suggested Citation

  • Pang, Ran & Zhang, Caizhi & Dai, Haifeng & Bai, Yunfeng & Hao, Dong & Chen, Jinrui & Zhang, Bin, 2022. "Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921010825
    DOI: 10.1016/j.apenergy.2021.117735
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    Cited by:

    1. Huang, Weifeng & Niu, Tong & Zhang, Caizhi & Fu, Zuhang & Zhang, Yuqi & Zhou, Weijiang & Pan, Zehua & Zhang, Kaiqing, 2023. "Experimental study of the performance degradation of proton exchange membrane fuel cell based on a multi-module stack under selected load profiles by clustering algorithm," Energy, Elsevier, vol. 270(C).
    2. Deng, Shutong & Zhang, Jun & Zhang, Caizhi & Luo, Mengzhu & Ni, Meng & Li, Yu & Zeng, Tao, 2022. "Prediction and optimization of gas distribution quality for high-temperature PEMFC based on data-driven surrogate model," Applied Energy, Elsevier, vol. 327(C).
    3. Young Park, Jin & Seop Lim, In & Ho Lee, Yeong & Lee, Won-Yong & Oh, Hwanyeong & Soo Kim, Min, 2023. "Severity-based fault diagnostic method for polymer electrolyte membrane fuel cell systems," Applied Energy, Elsevier, vol. 332(C).
    4. Danqi Su & Jiayang Zheng & Junjie Ma & Zizhe Dong & Zhangjie Chen & Yanzhou Qin, 2023. "Application of Machine Learning in Fuel Cell Research," Energies, MDPI, vol. 16(11), pages 1-32, May.

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