Sister Projects

The OPINCHARGE project focuses on advancing the understanding and optimisation of lithium-ion batteries (LIBs). It exemplifies European collaboration between diverse experts and organisations, integrating cutting-edge science and innovation, primarily to revolutionise battery technology for a greener and more sustainable future.

By fostering partnerships among European projects, research centers, academy, scientific community, battery industry, and local communities, the OPINCHARGE project aims to:

  • gain a deeper understanding of the interfacial phenomena,
  • improve the design and functionality of LIBs,

both leading to safer, more efficient, and longer-lasting battery systems across Europe.

Contact us if you are a representative of a relevant project that would like to explore partnering possibilities!

Contact information

Project coordinator:
Santhana Eswara Moorthy, santhana.eswara@list.lu  

OPERA

Green, safe and high-performing batteries based on abundant materials are a key element in the transition to a carbon-neutral future. So-called zero excess solid-state batteries are one of the most promising technologies that meets these criteria.

For their development, the processes within the battery need to be understood on a fundamental level. Therefore, in OPERA, state-of-the-art experimental and computational methods are combined to gain these insights by partners from all over Europe.

The consortium includes seven leading research institutes, two synchrotron radiation facilities, a small-medium sized enterprise and a large technological company, all from complementary research fields. They proposed a unique strategy to face the challenges of this technology. It relies on the development of novel experimental techniques with a resolution down to the atomic scale. They provide information on multiaxial stress fields, chemical composition, nucleation and growth kinetics, structural defect formation and degradation of model cells. These insights are used for a novel multiscale modelling approach supported by machine-learning algorithms.

The project goals ultimately lead to a deep conceptual understanding and innovative improvement approaches for this type of energy storage technology. This will be an important step towards increasing the global competitiveness, resilience, and independence of the EU.

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