GSS EU Science Funding Gateway

The following activities are within the scope of this topic:

• Gather data and a return on experiment available from previous or ongoing projects and/or data available at electrolysis manufacturers having already installed/operated electrolysers, including with RES sources at various scales;
• Identification of lifetime restricting degradation mechanisms and effects of their superposition by modelling and simulation activities validated by relevant experimental methods;
• Evaluate the impact of RES electrical profile on electrolysers durability in terms of the dynamic operating conditions;
• Modelling of degradation rates resulting from different degradation phenomena and operating conditions; models should be able to aggregate a variety of degradation mechanisms occurring at various scales and should be validated by experimental data;
• With the support of dynamic modelling, simulation of the transient electrical and thermal behaviour in view of the impacts on degradation effects;
• Development of lifetime prediction models based on the degradation modelling; proposals may include verification testing for such models for selected technologies;
• Development of operation solutions diminishing the degradation in stationary or transient operations (e.g. novel operating and control strategies, diagnostics etc.);
• When relevant, adaptation or improvement of advanced characterisation methods for deeper understanding by in-situ, ex-situ or in-operando analyses will be considered within scope;
• Validation of novel solutions in short stack level (minimum 5 repeating units) for at least 10,000 hours by meeting degradation rate while keeping similar level of performance (current density, hydrogen production rate) or in accelerated stress tests allowing extrapolation to minimum 40,0 00 hours. New and/or adapted stress tests protocols for unravelling superimposed effects are also within the scope;
• Development of uniform data reporting formats that can potentially be used for machine learning and big data processing to identify and correlate cause and effect of degradation phenomena;
• Assessment of the improved durability on the lifecycle impact of the selected technologies.