BLESSED Bridging Models at Different Scales To Design New Generation Fuel Cells for Electrified Mobility

Summary

To achieve the goals of the European Green Deal on climate neutrality, a 90% reduction in transport emissions is needed by 2050. The automotive industry urgently needs to accelerate the introduction of alternative powertrains for electrified vehicles. Hydrogen-powered Proton Exchange Membrane Fuel Cells (PEMFCs) are carbon-free power devices that meet these goals in both mobile and stationary applications. BLESSED aims at revolutionising the design process of next generation PEMFCs, to improve efficiency, durability and affordability for widespread use, with direct implications in clean energy and sustainable industry/mobility. BLESSED will train 15 Doctoral Candidates (DCs) to solve Multi-Scale (MS) engineering challenges, from the electrons up to the device level, through a unique combination of multi-disciplinary computational methods with Machine Learning (ML) to bridge each length scale’s highly accurate model to adjacent scales. Then, a top-down length scale approach will be followed to optimise PEMFC and its components. To this end, the 15 DCs will synergistically develop a unique MS computational framework for the all-scale PEMFC analysis/design, assisted by ML tools. This will allow the simultaneous consideration of complex physico-chemical phenomena occurring at all length scales, such as catalytically-assisted chemical reactions, contact of rough surfaces, mechanical/chemical degradation of membranes, fluid flows in porous media etc., at affordable computational cost. The proposed ID-network brings together world-class academic expertise on numerical modelling and simulation in electrochemistry, reacting flows, fluid mechanics, materials, optimisation methods and ML, with industrial developers. With a strong focus on industrial applications, BLESSED will develop methodologies and tools to exceed state-of-the-art in PEMFCs by minimising the Platinum group metal content and corrosion while maximising mass transport and electrical conductivity.

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