The OPINCHARGE Project held its project general meeting from November 21 to 22, 2024, hosted by Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL) in Aachen, Germany. This pivotal event united leading research institutions, experts in the field of battery technology and business consulting organisations to:

• review progress

• share insights

• plan future advancements and shape the future of energy storage.

Presentations from the Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL) and their partners highlighted significant work, achievements and opportunities in sustainable battery research world.

OPINCHARGE Progress Reports: Project partners presented updates on seven work packages:

• WP1: Project Management and Coordination (Leader: LIST)

• WP2: Interfacial structure and composition dynamics: a multiscale approach (Leader: CNRS)

• WP3: Operando, in-situ, and ex-situ characterization of interfaces using isotopes (Leader: LIST)

• WP4: Methods for low-dose high-speed data acquisition and data treatment (Leader: DFKI)

• WP5: Interface modelling and validation using experimental charge transport characteristics (Leader: DLR)

• WP6: Research Data Management, FAIR principles, Liaison with Battery 2030+ (Leader: CEDETEC)

• WP7: Dissemination, Exploitation & Communication (Leader: PEDAL)

Lab Tour:
An insightful visit to CARL’s facilities showcased work of advanced tools, including electron microscopes, computer tomographs, X-ray, and mass spectrometers, enabling the analysis of electronics and batteries from nanoscale particles to systems weighing hundreds of kilograms. The lab offers over 5,000 test circuits for cycling battery cells, accommodating currents from microamps to 1,000 amps and voltages up to 1,000 volts. These are used for life tests and electrical characterisation of battery systems. All test and analysis data are recorded in robust databases, enabling advanced analysis and correlations using artificial intelligence and machine learning.