Francisco Fernandez

f.fernandez@u-picardie.fr

322825335

Francisco
Fernandez

• Post-Doc

• Linkedin
• 
  https://github.com/fernandezfran
• 
  https://orcid.org/0000-0002-4052-5421

Tél. : 0322825335

Contact

Appeler

Thématique(s)

• Electrochimie et optimisation des dispositifs pour le stockage électrochimique

Descriptif des activités de recherche

I am developing a multi-fidelity modeling approach for the electrode manufacturing process of lithium ion battery cells. For this purpose I combine particle dynamics, discrete element, finite element, and machine learning. This models are obtained with softwares, like LAMMPS, and calibrated, trained, and validated with experimental data.

Parcours

PhD (Laboratorio de Energías Sustentables (LaES), Dr. Ezequiel Leiva & Dr. Daniel Barraco, 2019-2024, Computational modeling of lithium-ion battery electrodes)

Compétences

Particle dynamics, discrete element, molecular dynamics and machine learning

Projets en cours

European DIGICELL project

Digital correlation analysis and optimization of microporous layer through a machine learning workflow for PEMFC applications

Rashen Lou Omongos, Diego E. Galvez-Aranda, Francisco Fernandez, András Vernes, Alejandro A. Franco

Journal of Power Sources,

2025

Physics-Assisted Machine Learning for the Simulation of the Slurry Drying in the Manufacturing Process of Battery Electrodes: A Hybrid Time-Dependent VGG16-DEM Model

Diego E. Galvez-Aranda, Francisco Fernandez, Alejandro A. Franco

ACS Applied Materials & Interfaces,

2025

Transfer learning assessment of small datasets relating manufacturing parameters with electrochemical energy cell component properties

Francisco Fernandez, Soorya Saravanan, Rashen Lou Omongos, Javier F. Troncoso, Diego E. Galvez-Aranda and Alejandro Franco

npj computational materials,

2025

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Publications

Surrogate Modeling of Lithium-Ion Battery Electrode Manufacturing by Combining Physics-Based Simulation and Deep Learning

3D Resolved Computational Modeling to Simulate the Electrolyte Wetting of a Lithium-Ion Battery Cell with 18650 Format

A microstructure-resolved model of sodium-ion battery hard carbon electrodes

Digital correlation analysis and optimization of microporous layer through a machine learning workflow for PEMFC applications