Descrizione del progetto
A caccia della composizione della batteria di flusso di prossima generazione
CompBat intende portare le batterie di flusso a un livello successivo, identificando nuove possibili molecole per la loro composizione chimica. A tal fine verranno sviluppati progetti, utilizzando l’apprendimento automatico abbinato al metodo di screening ad alte prestazioni per consentire collaudi automatizzati su vasta scala. Le molecole mirate sono composti organici ispirati alla natura, o derivate da sostanze chimiche specifiche prodotte sfuse. Per raccogliere dati sulle molecole e sulle loro proprietà saranno impiegati calcoli sofisticati. In base ai risultati, il progetto CompBat, finanziato dall’UE, eseguirà una modellazione di sistemi di batterie di flusso per poter predire le prestazioni e per applicare un approccio di stima dei costi. Inoltre il team esaminerà la possibilità di utilizzare survoltori solidi per migliorare la capacità della batteria.
Obiettivo
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.
Campo scientifico
Parole chiave
Programma(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-LC-BAT-2019
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
02150 Espoo
Finlandia