Operating conditions combination analysis method of optimal water management state for PEM fuel cell

• Published in: Green Energy and Intelligent Transportation Vol. 2; no. 4; p. 100105
• Main Authors: Wan, Wenxin, Yang, Yang, Li, Yang, Xie, Changjun, Song, Jie, Deng, Zhanfeng, Tan, Jinting, Zhang, Ruiming
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023; Elsevier
• Subjects: Internal resistance-operating condition model; Operating condition optimization; PEMFC; Response surface method; Water management statue; Water management statue; PEMFC; Internal resistance-operating condition model; Operating condition optimization; Response surface method
• ISSN: 2773-1537; 2097-2512; 2773-1537
• DOI: 10.1016/j.geits.2023.100105

The water content of proton exchange membrane fuel cells (PEMFCs) affects the transport of reactants and the conductivity of the membrane. Effective water management measures can improve the performance and extend the lifespan of the fuel cell. The water management state of the stack is influenced by various external operating conditions, and optimizing the combination of these conditions can improve the water management state within the stack. Considering that the stack's internal resistance can reflect its water management state, this study first establishes an internal resistance-operating condition model that considers the coupling effect of temperature and humidity to determine the variation trend of total resistance and stack humidity with single-factor operating conditions. Subsequently, the water management state optimization method based on the ANN-HGPSO algorithm is proposed, which not only quantitatively evaluates the influence weights of different operating conditions on the stack's internal resistance but also efficiently and accurately obtains the optimal combination of five operating conditions: working temperature, anode gas pressure, cathode gas pressure, anode gas humidity, and cathode gas humidity to achieve the optimal water management state in the stack, within the entire range of current densities. Finally, the response surface experimental results of the stack also validate the effectiveness and accuracy of the ANN-HGPSO algorithm. The method mentioned in this article can provide effective strategies for efficient water management and output performance optimization control of PEMFC stacks. [Display omitted] •Internal resistance-operating condition model to assess humidity state.•Impact trends of operating conditions on water management state.•Impact weights of each operating condition on water management state.•Optimal operating combination for the best water management state.•Higher efficiency and accuracy compared to response surface methodology results.