ABSTRACT
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. The hydrogen-powered Proton Exchange Membrane Fuel Cells (PEMFCs) are carbon-free power devices for both mobile and stationary applications but are currently lacking in efficiency, durability, and affordability for widespread use. BLESSED aims at revolutionizing the design process of next-generation PEMFCs, for max efficiency and longevity, with direct implications for clean energy and sustainable industry/mobility. BLESSED will train a generation of future researchers to solve Multi-Scale (MS) engineering challenges, from the electrons up to the device level, through a unique combination of multi-disciplinary numerical methods. Each length scale’s highly accurate method will be bridged to adjacent scales via Machine Learning (ML). Then, a top-down approach will be followed to optimize PEMFC and its components. The 15 Doctoral Candidates will synergistically develop a unique MS computational framework, using ML tools to further accelerate the all-scale PEMFC development, which is currently slowed down by the simultaneous consideration of all complex physicochemical phenomena occurring at all length scales, such as catalytically-assisted chemical reactions, contact of rough surfaces and gas flows in porous materials. The proposed ID-network brings together world-class academic expertise on numerical modeling and simulation in electrochemistry, reacting flows, fluid mechanics, materials, optimization techniques, and ML with industrial developers. With a strong focus on industrial applications, BLESSED will develop analysis/design methodologies to overcome the current performance limitations of PEMFCs by minimizing the Platinum group metal content and corrosion or maximizing mass transport and electrical conductivity.
OBJECTIVES
In view of the above, the automotive industry urgently needs to accelerate the introduction of alternative (FC driven) powertrains for electrified vehicles. Hydrogen-powered FCs are carbon-free power options for both mobile and stationary applications; however, they currently lack power density, efficiency and durability for widespread use. Such requirements for efficient FC devices lead to an inevitable demand for an innovative integrated cross-cutting research programme for rapid development. This is the key objective of BLESSED. The fact that FC devices are Multi-Scale (MS) systems introduces unique challenges to their analysis & optimisation which have generally limited the pace of engineering development. Indeed, they comprise many concurrent physico-chemical phenomena at different length scales, such as catalytically-assisted electro chemical reactions among molecules at nm-scale, transport of protons & gases at micro-scale and gas flows at cm-scales. For an efficient energy conversion, optimal synergy of phenomena occurring at all scales in a FC is needed along with their understanding by future researchers/engineers. BLESSED proposes to build methodological links of MS problems for integrating research findings from all these phenomena and improving industrial applications. BLESSED will address challenges outlined by the Green Deal for the transportation sector, with direct implication in four of the seven policy areas of the Green Deal, namely clean energy, sustainable industry, sustainable mobility and eliminating pollution. This is why the consortium brings together world-class academic expertise on simulations of quantum mechanics, electrochemistry, Molecular Dynamics (MD), chemically reacting flows, Computational Fluid Dynamics (CFD)-based analysis and optimisation (incl. adjoint methods and Evolutionary Algorithms (EAs)), and Machine Learning (ML), with industrial developers, to revolutionise the design process of next generation PEMFC devices.
Bridging Models at Different Scales to Design New Generation Fuel Cells for Electrified Mobility 