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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.
Rock Eval®, a tool to characterize biochar
Biochar is a solid, carbon-rich (>60%) residue obtained via the pyrolysis of biomass. It is generally produced for spreading on agricultural land with a view to increasing carbon storage by the soil.
Issue 56 of Science@ifpen - PhD projects and Yves Chauvin Prize
2024 Yves Chauvin Prize: interview with the laureate, Mohammed Bin Jassar
Pagination
Pagination
FUNDAMENTAL RESEARCH, THE BUILDING BLOCK FOR FUTURE INNOVATION
Objectives pursued, scientific challenges to be overcome, partnerships proposed: watch a video on IFPEN’s fundamental research strategy.
To find out moreResearch in a few figures
- 1,095R&I engineers and technicians
- 30%of budget dedicated to fundamental research
- 9disciplinary fields
- 15active fundamental research framework agreements