"This is a huge challenge because the development of new batteries takes quite a long time using the current methods. In the BIG-MAP project, we want to accelerate this process significantly," says Professor Maximilian Fichtner, scientific spokesman of CELEST and POLiS and Vice Executive Director at Helmholtz Institute Ulm (HIU), which KIT founded together with the Ulm University. The purpose of the BIG-MAP (BIG stands for Battery Interface Genome; MAP for Materials Acceleration Platform) EU project is to establish completely new methods and thus significantly boost battery development – among other things through systematic automation and the use of artificial intelligence (AI). In future, the methods established in BIG-MAP will speed up the development of sustainable and ultra-high performance batteries by a factor of ten. "However, our vision is not only to be able to develop new batteries much faster, but also to ensure that they can store energy efficiently and that they can be produced in a sustainable manner and at such a low cost that, in future, it will be even more attractive to store electricity, for example from the sun and wind, in batteries," Fichtner explains. "A realignment of the existing discovery, development, and manufacturing processes for battery materials and technologies is necessary to enable Europe to rival its main competitors in the US and Asia."
"With BIG-MAP, we will have to reinvent the way we develop batteries. Last year, the Nobel Prize for Chemistry was awarded to the inventors of the lithium-ion battery. It was a fantastic invention, but it took 20 years from idea to product – we need to be able to do it in a tenth of that time if we want to provide sustainable batteries for the energy turnaround," says Tejs Vegge, professor at DTU and head of BIG-MAP.
The BIG-MAP project aims to create a common European data infrastructure that will enable us to autonomously collect and process data from all stages of the battery development cycle and then use them in cooperative workflows. Physical access to the differently equipped test facilities will then hardly be necessary for BIG-MAP researchers any longer, and they will be able to collaborate across national borders and time zones. AI-orchestrated experiments and synthesis will be based on huge amounts of collected data, with a focus on battery materials, interfaces, and intermediate phases. The data will be generated by computer simulations, autonomous high-throughput material synthesis and characterization, in operando experiments, and device-level tests. Novel AI-based tools and models will use the data to "learn" how battery materials and interfaces interact, thus laying the foundation for the improvement of future battery materials, interfaces, and cells.
COMPAMED-tradefair.com; Source: Karlsruhe Institute of Technology
