TY - JOUR
T1 - Alkaline electrolysis for green hydrogen production
T2 - A novel, simple model for thermo-electrochemical coupled system analysis
AU - Jin, Lingkang
AU - Nakashima, Rafael Nogueira
AU - Comodi, Gabriele
AU - Frandsen, Henrik Lund
PY - 2025
Y1 - 2025
N2 - Alkaline water electrolysis (AWE) is the most mature electrochemical technology for hydrogen production from renewable electricity. Thus, its mathematical modeling is an important tool to provide new perspectives for the design and optimization of energy storage and decarbonization systems. However, current models rely on numerous empirical parameters and neglect variations of temperature and concentration alongside the electrolysis cell, which can impact the application and reliability of the simulation results. Thus, this study proposes a simple four-parameter semi-empirical model for AWE system analysis, which relies on minimal fitting data, while providing reliable extrapolation results. In addition, the effect of model dimensionality (i.e., 0D, 1/2D and 1D) are carefully assessed in the optimization of an AWE system. The results indicate that the proposed model can accurately reproduce literature data from four previous works (R2 ≥ 0.98), as well as new experimental data. In the system optimization, the trade-offs existing in the lye cooling sizing highlight that maintaining a low temperature difference in AWE stacks (76-80°C) leads to higher efficiencies and lower hydrogen costs.
AB - Alkaline water electrolysis (AWE) is the most mature electrochemical technology for hydrogen production from renewable electricity. Thus, its mathematical modeling is an important tool to provide new perspectives for the design and optimization of energy storage and decarbonization systems. However, current models rely on numerous empirical parameters and neglect variations of temperature and concentration alongside the electrolysis cell, which can impact the application and reliability of the simulation results. Thus, this study proposes a simple four-parameter semi-empirical model for AWE system analysis, which relies on minimal fitting data, while providing reliable extrapolation results. In addition, the effect of model dimensionality (i.e., 0D, 1/2D and 1D) are carefully assessed in the optimization of an AWE system. The results indicate that the proposed model can accurately reproduce literature data from four previous works (R2 ≥ 0.98), as well as new experimental data. In the system optimization, the trade-offs existing in the lye cooling sizing highlight that maintaining a low temperature difference in AWE stacks (76-80°C) leads to higher efficiencies and lower hydrogen costs.
KW - Power-to-Hydrogen
KW - Alkaline electrolysis
KW - Temperature control
KW - Levelized cost of hydrogen
KW - Hydrogen production
U2 - 10.1016/j.applthermaleng.2024.125154
DO - 10.1016/j.applthermaleng.2024.125154
M3 - Journal article
SN - 1359-4311
VL - 262
JO - Applied Thermal Engineering
JF - Applied Thermal Engineering
M1 - 125154
ER -