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State Estimation of Membrane Water Content of PEMFC Based on GA-BP Neural Network

Author

Listed:
  • Haibo Huo

    (Shanghai Engineering Research Center of Marine Renewable Energy, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Jiajie Chen

    (Shanghai Engineering Research Center of Marine Renewable Energy, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Ke Wang

    (Shanghai Engineering Research Center of Marine Renewable Energy, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Fang Wang

    (Shanghai Engineering Research Center of Hadal Science and Technology, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Guangzhe Jin

    (Shanghai Engineering Research Center of Marine Renewable Energy, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Fengxiang Chen

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

Abstract

Too high or too low water content in the proton exchange membrane (PEM) will affect the output performance of the proton exchange membrane fuel cell (PEMFC) and shorten its service life. In this paper, the mathematical mechanisms of cathode mass flow, anode mass flow, water content in the PEM and stack voltage of the PEMFC are deeply studied. Furthermore, the dynamic output characteristics of the PEMFC under the conditions of flooding and drying membrane are reported, and the influence of water content in PEM on output performance of the PEMFC is analyzed. To effectively diagnose membrane drying and flooding faults, prolong their lifespan and thus to improve operation performance, this paper proposes the state assessment of water content in the PEM based on BP neural network optimized by genetic algorithm (GA). Simulation results show that compared with LS-SVM, GA-BP neural network has higher estimation accuracy, which lays a foundation for the fault diagnosis, life extension and control scheme design of the PEMFC.

Suggested Citation

  • Haibo Huo & Jiajie Chen & Ke Wang & Fang Wang & Guangzhe Jin & Fengxiang Chen, 2023. "State Estimation of Membrane Water Content of PEMFC Based on GA-BP Neural Network," Sustainability, MDPI, vol. 15(11), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:9094-:d:1163957
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    References listed on IDEAS

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