Computer aided design
for next generation flow batteries
On the hunt for the next generation flow battery composition CompBat aims to take flow batteries to the next level, identifying new prospective molecules for their chemical composition. Tools will be developed to this end, using machine learning paired with a high-throughput screening method to enable large-scale automated testing.
Targeted molecules are bio-inspired organic compounds, as well as derivatives of a specialty bulk-manufactured chemical. Sophisticated calculations will be deployed to obtain data on molecules and their properties. Based on the results, The EU-funded CompBat project will perform modelling of flow battery systems to allow for predictions on performance, and a cost estimation approach will be applied.
Furthermore, the team will examine the possibility of using solid boosters to enhance battery capacity.
CompBat will focus on developing tools for discovery of new prospective candidates for next generation flow batteries, based on machine learning assisted high-throughput screening. Density functional theory calculations will be used to obtain data on solubilities and redox potentials of different molecules, and machine learning methods are used to develop high-throughput screening tools based on the obtained data. The results of the high-throughput screening are validated with experimental results. Target molecules will be bio-inspired organic compounds, as well as derivatives of the redox active specialty chemical already manufactured in bulk quantities.
Stability and reversibility of the molecules will also be investigated by DFT calculation, experimental investigations and machine learning methods, for a selected group of interesting molecules.
Numerical modelling of flow battery systems will be performed with finite element method, and with more general zero-dimensional models based on mass-transfer coefficients. The models will be verified experimentally, and the modelling will generate a data-set to allow prediction of the flow battery cell performance based on properties of the prospective candidates obtained from high-throughput screening. This data is used then to predict the flow battery system performance from the stack level modelling. Freely available cost estimation tools are then adapted to estimate the system performance also in terms of cost. This approach will allow prediction of the battery performance from molecular structure to cost.
Furthermore, the concept of using solid boosters to enhance the battery capacity will be investigated by developing models to simulate the performance of such a systems, and validating the models experimentally with the candidates already reported in the literature.
Jyväskylä preliminary results The Pihko group at Jyväskylä University (JYU) focuses on the chemical synthesis of new materials that could be utilized to store energy in flow batteries. We start from natural products, such as vitamins, that possess the requisite...
Electrochemical redox reactions kinetics modeling at the electrode-electrolyte interface We use quantum mechanical constraint density functional theory (CDFT) in combination with ab initio molecular dynamics (AIMD) to model the kinetics of electrochemical redox...
For Europe, the establishment of a complete domestic battery value chain is imperative for a clean energy transition and a competitive industry.
EUROPEAN BATTERY ALLIANCE
“For Europe, the establishment of a complete domestic battery value chain is imperative for a clean energy transition and a competitive industry.”
EUROPEAN BATTERY ALLIANCE
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875565. The content in this document represents the views of the authors, and the European Commission has no liability in respect of the content.