courbe en fond

Non permanents

Descriptif des activités de recherche

Nowadays, in the context of Electric Vehicle (EV) applications, there is a high interest of the good understanding of ageing processes. Multiphysics models describing with some detail ageing mechanisms can provide understanding of experimental observations but under the restrictions of the assumed mechanisms and at expenses of high computational cost. In the other hand, well-trained machine learning techniques can potentially combine high accuracy and low computational cost, making it highly interesting for ageing models and accurate predictions of battery lifetime.
The PhD thesis is a collaboration between Renault Technocentre and LRCS to develop and validate a hybrid methodology encompassing both, machine learning methods and Multiphysics modelling which will provide a highly accurate lithium-ion battery ageing model allowing earlier failure prediction, greater interpretability and broader application to a wide range of cycling conditions.

Supervisors: Prof. Alejandro A. Franco, Dr. Mohamed Ati (Renault Technocentre)


- Master degree in Mathematics, Applied Analysis and Modelling, UPJV, Amiens, France.
- Research internship within ARTISTIC project, supervised by Prof. Alejandro A. Franco and Prof. Mark Asch (LAMFA), LRCS, UPJV, Amiens.
Development of a surrogate machine learning model for Lattice Boltzmann code to optimize the electrolyte manufacturing parameters in Lithium-ion battery.
- Bachelor degree in Applied Mathematics, Mohammed V University, Rabat, Morocco.


Data analysis, machine learning techniques, programming, Multiphysics modelling

Projets en cours

CIFRE / Renault Technocentre

Machine learning for optimal electrode wettability in lithium ion batteries

Amina El Malki, Mark Asch, Oier Arcelus, Abbos Shodiev, Jia Yu, Alejandro A. Franco

Journal of Power Sources Advances, 2023

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