Project description
Renewable materials from lignocellulosic for flow batteries
The EU aims to transition to renewable energy grids, but faces challenges in achieving this goal, particularly meeting the need for scalable, secure, and sustainable electrical energy storage. This challenge is exacerbated by the dependence on critical raw materials sourced from politically unstable regions. The EU-funded VanillaFlow project will develop innovative methods for energy storage. This involves leveraging flow battery technology and integrating it with artificial intelligence and machine learning. The project’s vision includes the replacement of unsustainable and critical raw materials currently used in flow batteries, such as redox-active molecules and membranes, with renewable materials derived from starch and lignocellulosic sources that are readily available. VanillaFlow is poised to bolster European technological leadership by fostering collaboration across various fields.
Objective
The Grand Challenge ahead is to shift fossil-dominated centralized energy systems towards regenerative integrated multi-vector grids. This requires also sustainable electrical energy storage, including the related raw material supply, processes and systems. A real impact on economy, society and ecology is only created if materials, processes and products can be potentially transferred to large scale. This represents a particular challenge for mid to long term, systems-integrated energy storage, also because the EU strongly depends on critical raw materials from politically instable regions. In VanillaFlow, we develop radically new approaches for integrated energy storage which combine artificial intelligence (AI) and machine learning (ML) with flow battery technology to replace currently employed, non-sustainable, and critical raw materials (i.e. redox-active molecules, membranes) in flow batteries by readily-available renewable materials based on starch and lignocellulosics. VanillaFlow will use AI and ML techniques such as physics-informed modeling, causal discovery, and representation learning, and makes use of deep learning and symbolic regression. These approaches are used in designing redox active quinones, and to optimize their interplay with the other components of a battery on single and multi-cell level. The whole research will be guided by toxicology investigations to ensure that sustainable and inherently safe materials will be obtained in the project. Today, the innovation capacity of European scientists and industry in the area of renewable materials makes them already the leading global players in the field. VanillaFlow will further support the European technological leadership in the area by cross-fertilization of different fields (artificial intelligence, battery technology, pulp and paper, biotechnology, polymer technology, toxicity) while addressing needs of sustainable materials in mid to long term energy storage.
Fields of science
- natural scienceschemical scienceselectrochemistryelectric batteries
- natural sciencesbiological sciencesecology
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- medical and health sciencesbasic medicinetoxicology
- engineering and technologyenvironmental engineeringenergy and fuelsenergy conversion
Programme(s)
- HORIZON.3.1 - The European Innovation Council (EIC) Main Programme
Funding Scheme
HORIZON-EIC - HORIZON EIC GrantsCoordinator
8010 Graz
Austria