A dozen students from the Energy and Powertrains (MOT), Powertrain Engineering (PWT) and Energy and Products (PRO) programs proposed solutions to concrete problems in the powertrain sector, based on machine learning methods.
In Module 3, led by Alessio Dulbecco, a lecturer at IFP School's Powertrains and Sustainable Mobility Center, students worked on projects based on real data provided by Renault Group.
Using machine learning methods, they practiced data analysis to solve problems such as predicting the fuel consumption of a hybrid vehicle and determining oil change intervals.
Students had to create a video presentation to generate interest in their project. They then submitted these pitches the day before their defense. The latter took place in front of a jury made up of IFP School teachers, IFP Energies nouvelles experts and Renault Group representatives.
Watch the pitches: