Background information

Marie Skłodowska-Curie Doctoral Networks are joint research and training projects funded by the European Union. Funding is provided for doctoral candidates from both inside and outside Europe to carry out individual project work in a European country other than their own. The training network “PREDICTOR” is made up of 22 partners, coordinated by Fraunhofer ICT in Germany. The network will recruit a total of 17 doctoral candidates for project work lasting for 36 months.

PREDICTOR aims to establish a rapid, high-throughput method to identify and develop materials for electrochemical energy storage. It will enable the rapid identification, synthesis and characterization of materials within a coherent development chain, replacing conventional trial-and-error developments. To validate the PREDICTOR system, the case study will be active materials and electrolytes for redox-flow batteries. Within the project, three demonstrator battery cells (TRL3-4) will be assembled and tested with the newly developed materials.

Project objectives:

• A modelling and simulation tool for the computational screening of organic chemicals based on their potential performance in energy storage systems.
• Automated chemical synthesis, electrolyte production and characterization methods, so that the chemicals identified in the screening step can be rapidly produced and tested for their suitability in energy storage applications.
• Artificial- intelligence- based self-optimization methods that allow experimental data from material characterization to be fed back into automated experimental methods to enable self-driving laboratory platforms and for modelling and simulation tools, improving their accuracy.
• Data management systems to standardize and store the data generated for further use in model validation and self-optimization processes.

Job description
The advertized subproject is fully funded by the Marie Skłodowska-Curie European Training Network “PREDICTOR“. It will be carried out by one doctoral candidate at the Institute of Mechanical Process Engineering, PhD supervision under the guidance of Prof. Dr. Hermann Nirschl over a period of 36 months.

The Karlsruhe Institute of Technology (KIT) is “The Research University in the Helmholtz Association.” As the only German university of excellence with a national large-scale research sector, we offer our students, researchers, and employees unique learning, teaching, and working conditions.

In previous projects a framework for the simulation-based screening of electroactive materials for aqueous and nonaqueous organic redox flow batteries (RFBs) has been developed. It adopted a multiscale modelling paradigm, in which simulation methods at different physical scales were further advanced and linked by combining physics- and data-based modelling. The next step is the implementation of control concepts for single- and multi-stage processes (process chain). This will involve the automatic adjustment of power behaviour, material properties, compensation of unforeseen disturbances, acceleration of start-up and shut-down, and planning and control of optimal trajectories under consideration of uncertainties and process relevant quality criteria (e.g. energy and raw material efficiency). Nonlinear, optimization based and robust control methods will be applied. This is a new challenge for RFB and the topic for an innovative Ph.D. thesis.

Salary
The Doctoral Network “PREDICTOR” is financed by the European Union under the framework of the program HORIZON Europe, Marie Skłodowska-Curie Actions. The doctoral candidate will be hired for 36 months under contract by the Karlsruhe Institute of Technology, with a monthly gross salary of app. 3.200,-€