Different Approaches to Predict and Optimize an SOC Performance: Experimental and Numerical Study

Abstract

Solid oxide cells (SOC) provide a great potential to enable desired transition to an efficient, environmentally friendly and low-carbon energy system. Their great advantage is the capability of a reversible operation, which enables to utilize fuels to generate electricity and heat, or in a reversible mode to utilize electricity, steam and carbon dioxide to generate valuable fuels. Nevertheless, in order to accelerate their commercialization, the main criteria such as reliability, stability and degradation prevention at its preliminary stage have to be ensured. In order to fulfil these requirements, different optimization steps are necessary, starting with the material development, over cell, stack and system manufacturing, towards optimized operating environment. In this study we applied different approaches to (i) predict and optimize the cell performance, (ii) predict different failure modes, and thus to (iii) prolong the SOC lifetime. A design of experiment was made in order to cover a range of different parameters and to use them for the further model validation. The experimental investigations have been performed employing both conventional and non-conventional methodologies, e.g. total harmonic distortion (THD) analysis. For the purpose of experimental investigations, the cell was fuelled with humidified hydrogen or humidified methane, whereby to generate hydrogen purely steam electrolysis was employed. Different parameters such as operating temperature, fuel volume flow, etc. were varied and their impact on the cell performance was studied. On the other hand, two different modelling approaches were considered in order to predict and optimize the cell performance. The first one is based on a physical model, which enables very precise performance prediction, and suggests the required optimization steps. The second one is based on the artificial intelligence, which enables to determine best appropriate operating conditions using the information from previous experiments carried out. As an illustrative example, Fig. 1 compares different approaches, such as the temperature monitoring over the entire cell surface and the electrochemical impedance spectra both simulated and subsequently validated using the experimental data. Within this study we show that the cell performance can be optimized employing different simulation aspects with the aim of time-saving calculations. Moreover, when employing non-conventional online monitoring tools, an early degradation prediction is possible thus significantly prolonging the cell overall lifetime. Figure 1