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.
