Binyamin Binyamin, Lim Ocktaeck
Graduate School of Mechanical Engineering, University of Ulsan, DaeHak-ro 93, Nam-gu, Ulsan 44610, South Korea.
Department of Mechanical Engineering, Universitas Muhammadiyah Kalimantan Timur, Samarinda 75124, Indonesia.
ACS Omega. 2023 Dec 18;9(1):1516-1534. doi: 10.1021/acsomega.3c07932. eCollection 2024 Jan 9.
Temperature distribution, mass transport, and current density are crucial parameters to characterize the durability and output performance of proton exchange membrane fuel cell (PEMFC), which are affected by thermal contact resistance (TCR) and gas diffusion layer (GDL) face permeability within both cathode and anode GDL porous jumps. This study examined the effects of TCR and GDL face permeability on a single PEM fuel cell's temperature profiles, mass transport, and cell performance using a three-dimensional, nonisothermal computational model with an isotropic gas diffusion layer (GDL). This model calculates the ideal thermal contact resistance by comparing the expected plate-cathode electrode temperature difference to the numerical and experimental literature. The combined artificial neural network-genetic algorithm (ANN-GA) method is also applied to identify the optimum powers and their operating conditions in six cases. Theoretical findings demonstrate that TCR and suitable GDL face permeability must be considered to optimize the temperature distribution and cell efficiency. TCR and GDL face permeability lead to a 1.5 °C rise in maximum cell temperature at 0.4 V, with a "Λ" shape in temperature profiles. The TCR and GDL face permeability also significantly impacts electrode heat and mass transfer. Case 6 had 1.91, 6.58, and 8.72% higher velocity magnitudes, oxygen mass fractions, and cell performances than case 1, respectively. Besides, the combined ANN-GA method is suitable for predicting fuel cell performance and identifying operation parameters for optimum powers. Therefore, the findings can improve PEM fuel cell performance and give a reference for LT-PEMFC design.
温度分布、质量输运和电流密度是表征质子交换膜燃料电池(PEMFC)耐久性和输出性能的关键参数,它们受阴极和阳极气体扩散层(GDL)多孔跃变内的热接触电阻(TCR)和GDL面渗透率的影响。本研究使用具有各向同性气体扩散层(GDL)的三维非等温计算模型,研究了TCR和GDL面渗透率对单个PEM燃料电池温度分布、质量输运和电池性能的影响。该模型通过将预期的极板 - 阴极电极温差与数值和实验文献进行比较来计算理想的热接触电阻。还应用了组合人工神经网络 - 遗传算法(ANN - GA)方法来确定六种情况下的最佳功率及其运行条件。理论研究结果表明,必须考虑TCR和合适的GDL面渗透率来优化温度分布和电池效率。TCR和GDL面渗透率会导致在0.4 V时电池最高温度升高1.5°C,温度分布呈“Λ”形。TCR和GDL面渗透率也对电极的传热和传质有显著影响。案例6的速度大小、氧气质量分数和电池性能分别比案例1高1.91%、6.58%和8.72%。此外,组合的ANN - GA方法适用于预测燃料电池性能并确定最佳功率的运行参数。因此,这些研究结果可以提高PEM燃料电池性能,并为低温PEMFC设计提供参考。