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Battery Pack
Fundamental Research
News 27 February 2025

Using AI to predict battery aging

To meet the challenges of climate change, the transition to renewable energies and the decarbonization of transport are essential, with electric mobility and lithium-ion batteries playing a central role. Battery aging is a complex phenomenon, driven by numerous factors, and requires robust models to predict and optimize their use. Quentin Mayemba’s PhD research resulted in the development of an innovative general machine learning model capable of adapting to various datasets to predict battery aging. These contributions, which are invaluable to the scientific community, provide solid tools and open up new avenues for the development of methodologies tailored to the study of lithium-ion batteries.

Electrodes
Fundamental Research
News 27 February 2025

Lithium visualization in battery electrodes to understand ageing mechanisms

In recent years, lithium-ion batteries, used in a variety of electronic devices and vehicles, have been facing challenges in terms of sustainability and lithium availability, thereby necessitating a better understanding of their ageing mechanisms. To this end, the ANR Micro-Q-Li project, conducted in partnership with the French Institute of Light and Matter, developed an enhanced LIBS imaging prototype that achieves a spatial resolution of 1.5 µm, exceeding the limitations of traditional analytical techniques for lithium imaging.