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Multi-objective optimization of helical baffle flow field structure for fuel cell under multiple performance indicator constraints

Author

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  • Liu, Qingshan
  • Lan, Fengchong
  • Wang, Junfeng
  • Chen, Yisong
  • Chen, Jiqing

Abstract

To enhance the output performance and durability of fuel cells (FC), a novel helical baffle flow field (FF) is proposed, which can enhance mass transfer in both in-plane and through-plane directions. To improve the efficiency of FF structure design and optimization, and to further optimize FF structure to obtain the best mass transfer capability, an efficient and high-precision optimization analysis process is constructed. The sample data set of the baffle is obtained using optimal Latin hypercube sampling. The established numerical model was used to calculate the FC performance under different baffle structures, and artificial neural network surrogate models were trained to accurately predict two performance indicators under two relative humidity (RH) conditions, namely area and mass power density. The baffle structure parameters and output values that maximize the two performance indicators are obtained for different RH cases. Relative to the original FF, the new FF can make a 5.12 %∼12.68 % improvement in the FC performance. Since the two performance indicators are contradictory and constrained, to obtain the baffle structure parameters with better performance under different conditions, the FC's comprehensive performance under multi-objective performance constraints is optimized using a genetic algorithm to obtain the optimal baffle structure parameters under the specified conditions.

Suggested Citation

  • Liu, Qingshan & Lan, Fengchong & Wang, Junfeng & Chen, Yisong & Chen, Jiqing, 2025. "Multi-objective optimization of helical baffle flow field structure for fuel cell under multiple performance indicator constraints," Renewable Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:renene:v:241:y:2025:i:c:s0960148125000114
    DOI: 10.1016/j.renene.2025.122349
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    References listed on IDEAS

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    1. Lian, Yunsong & Zhu, Zhengchao & You, Changtang & Lin, Liangliang & Lin, Fengtian & Lin, Le & Huang, Yating & Zhou, Wei, 2023. "Structural optimization of fiber porous self-humidifying flow field plates applied to proton exchange membrane fuel cells," Energy, Elsevier, vol. 271(C).
    2. Najmi, Aezid-Ul-Hassan & Anyanwu, Ikechukwu S. & Xie, Xu & Liu, Zhi & Jiao, Kui, 2021. "Experimental investigation and optimization of proton exchange membrane fuel cell using different flow fields," Energy, Elsevier, vol. 217(C).
    3. Ye, Lingfeng & Qiu, Diankai & Peng, Linfa & Lai, Xinmin, 2024. "Conduction mechanism analysis and modeling of different gas diffusion layers for PEMFC to improve their bulk conductivities via microstructure design," Applied Energy, Elsevier, vol. 362(C).
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    Cited by:

    1. Jiang, Ke & Liang, Zhendong & Jiang, Haolin & Luan, Yang & Su, Xunkang & Zheng, Tongxi & Liu, Mingxin & Feng, Yihui & Li, Wenfei & Chen, Yongbang & Lu, Guolong & Liu, Zhenning, 2025. "Systemic comparison of machine learning models in the optimization of flow field design for proton exchange membrane fuel cells," Energy, Elsevier, vol. 335(C).
    2. Shixin Li & Qingshan Liu & Yisong Chen, 2025. "Synergistic Framework for Fuel Cell Mass Transport Optimization: Coupling Reduced-Order Models with Machine Learning Surrogates," Energies, MDPI, vol. 18(10), pages 1-19, May.

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