• Baudrin, Emmanuel
• Wolf, Amadeus
• Wlodarczyk, Jakub
• Gerlach, Daniel
• Franco, Alejandro A.
• Roznyatovskaya, Nataliya
• Skyllas-Kazacos, Maria
• Silva, Piotr De
• Fornari, Rocco
• Schaerer, Roman
• Guan, Xinjie
• Maaß, Astrid
• Menictas, Chris
• Mourouga, Gael
• Schumacher, Jürgen
• Hamaekers, Jan
• Yu, Jia
• Nirschl, Hermann

Abstract

<jats:p>Due to the characteristics of flow batteries, this technology is ideally suited for low-cost storage in the range of a few hours and thus for load balancing as stationary storage in grids with high amounts of renewable energy <jats:uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#_ftn1" xlink:type="simple">[1]</jats:uri>. Today, a large number of different active materials for flow batteries are known, although only a few have been commercialised <jats:uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#_ftn2" xlink:type="simple">[2]</jats:uri>. Basically, the energy supply and thus also the required storage should be sustainable, i.e. not cause resource problems and not be harmful to humans and the environment. A potential for a huge range of possibilities is offered by organic active materials, which should be used especially in aqueous solutions. Due to the immense possibilities, classical synthesis and testing is extremely lengthy and costly. An alternative can be model-based high-throughput screening, where by simulating the properties of active materials in the electrolyte and the battery itself, computer-based simulations can be used to conduct the search.</jats:p><jats:p>The SONAR project is an EU-funded project in which 7 different institutions from the EU, Switzerland and Australia are developing a high-throughput screening method capable of finding new active materials for redox flow batteries. The principle is a serial coupling of different size scales, combined with molecule generation and machine learning. The chemical structure of a candidate is generated by a molecule generator and then its atomistic properties, kinetics, side reactions and cell properties are iteratively calculated with exclusion criteria.</jats:p><jats:p>In this talk we will give an overview of 2 years of research in this project in the areas of machine learning for high throughput screening, DFT based quantum mechanics modelling, kinetics Monte Carlo methods for meso-scale, 0D cell modelling, 3D cell modelling, stack modelling and techno-economics.</jats:p><jats:p><jats:uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#_ftnref1" xlink:type="simple">[1]</jats:uri> B. Dunn, H. Kamath, J.-M. Tarascon, Science 2011, 334, 928–935.</jats:p><jats:p><jats:uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#_ftnref2" xlink:type="simple">[2]</jats:uri> J. Noack, N. Roznyatovskaya, T. Herr, P. Fischer, Angew. Chem. Int. Ed. 2015, 54, 9776–9809.</jats:p><jats:p><jats:inline-formula><jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="1954fig1.jpg" xlink:type="simple" /></jats:inline-formula></jats:p><jats:p>Figure 1</jats:p><jats:p />

Topics

• impedance spectroscopy
• simulation
• density functional theory
• Monte Carlo method
• machine learning