Performance map and operating condition optimization of industrial-size SOFCs using a PINN surrogate model

Abstract

Solid Oxide Fuel Cell (SOFC) is an energy conversion technology featuring high efficiency, power density, durability, and fuel compatibility. In practice, however, SOFCs reach high power and high efficiency at different operating conditions. Hence, the optimal feasible operating condition is not straightforward and requires optimization.To resolve this problem, an accurate pretrained Physics-Informed Neural Network (PINN) model was developed as a surrogate of a 2D multi-physics model. For calibration, a planar SOFC with a 10 × 10 cm2 active area was tested at varied operating conditions. The resulting voltage error of the surrogate model was as low as 0.513%. The average runtime of the PINN model was 0.5 ms per case. Moreover, the PINN model accepts cell performance parameters as input and is therefore highly flexible.The surrogate was thereafter employed to generate performance maps that visualize the steady operating states at each combination of H2 flowrate and operating voltage. The voltages of peak power points and anode-safety boundaries were plotted as functions of H2 flowrate, so that the optimal operating conditions are shown graphically and can be tracked conveniently as the cell degrades.Further studies involving hydrocarbon fuels will be carried out in the future.