Decoupling the influence of operating factors such as temperature, humidity, and back pressure is essential for improving fuel cell efficiency. This study employs a regression model combining Locally Weighted Scatterplot Smoothing (LOESS) with the Sobol index method to analyze the effects of two major parameter categories: temperature and humidity, and stoichiometry and back pressure, on fuel cell output voltage. Using limited experimental data, it uncovers complex interactions and sensitivities among these factors. The results indicate that the stack temperature has the greatest impact, accounting for 60%, with minimal influence from the changes in the current density. Cathode humidity impacts output voltage by about 10%, while anode humidity accounts for approximately 5%. Significant interactions between temperature and both anode and cathode humidity contribute around 9% each. Among stoichiometries, cathode stoichiometry has the largest impact, exceeding 50% at low current density and growing to over 70% as current increases. Back pressure and anode stoichiometry each have an impact of around 10%, with minimal mutual influence. This study highlights the extent to how different operating parameters influence fuel cell performance, offering valuable insights for optimizing fuel cell operating conditions.
RashidiSKarimiNSundenB, et al. Progress and challenges on the thermal management of electrochemical energy conversion and storage technologies: Fuel cells, electrolysers, and supercapacitors. Prog Energy Combust Sci2022; 88: 100966.
2.
YueMLambertHPahonE, et al. Hydrogen energy systems: a critical review of technologies, applications, trends and challenges. Renew Sustain Energ Rev2021; 146: 111180.
3.
ChenDPeiPLiY, et al. Proton exchange membrane fuel cell stack consistency: evaluation methods, influencing factors, membrane electrode assembly parameters and improvement measures. Energy Convers Manag2022; 261: 115651.
4.
MeiBBarnoonPToghraieD, et al. Energy, exergy, environmental and economic analyzes (4e) and multi-objective optimization of a PEM fuel cell equipped with coolant channels. Renew Sustain Energ Rev2022; 157: 112021.
5.
SalvaJAIranzoARosaF, et al. Validation of cell voltage and water content in a PEM (polymer electrolyte membrane) fuel cell model using neutron imaging for different operating conditions. Energy2016; 101: 100–112.
6.
OzenDNTimurkutlukBAltinisikK. Effects of operation temperature and reactant gas humidity levels on performance of PEM fuel cells. Renew Sustain Energ Rev2016; 59: 1298–1306.
7.
JeonDHKimKNBaekSM, et al. The effect of relative humidity of the cathode on the performance and the uniformity of PEM fuel cells. Int J Hydrogen Energy2011; 36(19): 12499–12511.
8.
KahveciEETaymazI. Assessment of single-serpentine PEM fuel cell model developed by computational fluid dynamics. Fuel2018; 217: 51–58.
9.
ZhangJTangYSongC, et al. PEM fuel cell relative humidity (rh) and its effect on performance at high temperatures. Electrochim Acta2008; 53(16): 5315–5321.
10.
KimHYKimK. Numerical study on the effects of gas humidity on proton-exchange membrane fuel cell performance. Int J Hydrogen Energy2016; 41(27): 11776–11783.
11.
ZhangJTangYSongC, et al. PEM fuel cells operated at 0% relative humidity in the temperature range of 23120. Electrochim Acta2007; 52(15): 5095–5101.
12.
XingLCaiQXuC, et al. Numerical study of the effect of relative humidity and stoichiometric flow ratio on PEM (proton exchange membrane) fuel cell performance with various channel lengths: an anode partial flooding modelling. Energy2016; 106: 631–645.
13.
WangLHusarAZhouT. A parametric study of PEM fuel cell performances. Int J Hydrogen Energy2003; 28(11): 1263–1272.
14.
AskaripourH. Effect of operating conditions on the performance of a PEM fuel cell. Int J Heat Mass Transf2019; 144: 118705.
15.
SongXZhangJZhanC, et al. Global sensitivity analysis in hydrological modeling: review of concepts, methods, theoretical framework, and applications. J Hydrol2015; 523: 739–757.
16.
NortonJ. An introduction to sensitivity assessment of simulation models. Environ Model Softw2015; 69: 166–174.
17.
LaounBNaceurMWKhellafA, et al. Global sensitivity analysis of proton exchange membrane fuel cell model. Int J Hydrogen Energy2016; 41(22): 9521–9528.
18.
ZhangSMaoYLiuF, et al. Multi-objective optimization and evaluation of PEMFC performance based on orthogonal experiment and entropy weight method. Energy Convers Manag2023; 291: 117310.
19.
ShaoQGaoEMaraT, et al. Global sensitivity analysis of solid oxide fuel cells with bayesian sparse polynomial chaos expansions. Appl Energy2020; 260: 114318.
20.
ZhouDTrang NguyenTBreazE, et al. Global parameters sensitivity analysis and development of a two-dimensional real-time model of proton-exchange-membrane fuel cells. Energy Convers Manag2018; 162: 276–292.
21.
FanRChangGXuY, et al. Investigating and quantifying the effects of catalyst layer gradients, operating conditions, and their interactions on PEMFC performance through global sensitivity analysis. Energy2024; 290: 130128.
22.
LiuYTuZChanSH. Water management and performance enhancement in a proton exchange membrane fuel cell system using optimized gas recirculation devices. Energy2023; 279: 128029.
23.
ChangYLiuJLiR, et al. Effect of humidity and thermal cycling on the catalyst layer structural changes in polymer electrolyte membrane fuel cells. Energy Convers Manag2019; 189: 24–32.
24.
XiaZChenHZhangR, et al. Multiple effects of non-uniform channel width along the cathode flow direction based on a single PEM fuel cell: an experimental investigation. J Power Sources2022; 549: 232080.
25.
YuanHDaiHMingP, et al. Quantitative analysis of internal polarization dynamics for polymer electrolyte membrane fuel cell by distribution of relaxation times of impedance. Appl Energy2021; 303: 117640.
26.
YuanHDaiHMingP, et al. Understanding dynamic behavior of proton exchange membrane fuel cell in the view of internal dynamics based on impedance. Chem Eng J2022; 431: 134035.
27.
ClevelandWS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc1979; 74(368): 829–836.
28.
ChenKLaghroucheSDjerdirA. Health state prognostic of fuel cell based on wavelet neural network and cuckoo search algorithm. ISA Trans2021; 113: 175–184.
29.
SaltelliAAnnoniPAzziniI, et al. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun2010; 181(2): 259–270.
30.
YangYHLinQZhangCF, et al. Study of the influence under different operating conditions on the performance of hydrogen fuel cell system for a bus. Process Saf Environ Prot2023; 177: 1027–1034.
