Descriptif des activités de recherche

Optimization of high-power battery cathodes through computational electrochemical modeling and machine learning frameworks. Current activities focus on developing 3D time-resolved computational models to analyze the impact of electrode architecture—including particle size, porosity, and thickness—on the electrochemical performance of sodium-ion batteries (SIB) for high-power applications. Work involves integrating experimental characterization (e.g., material and cell testing) to calibrate models, followed by deploying artificial intelligence/machine learning (AI/ML) frameworks for data augmentation and electrode architecture optimization. Partners include the Laboratoire de Réactivité et de Chimie des Solides (LRCS, UMR 7314, CNRS) at the University of Picardie-Jules Verne, with supervision from Dr. Alejandro Franco (UPJV). Collaborations also involve secondment to the Center for Solar Energy and Hydrogen Research (ZSW, Germany) for experimental validation and electrode fabrication. The goal is to establish correlations between electrode microstructure and performance metrics (energy density, power density, lifespan), enabling inverse design of high-power cathodes for both sodium-ion batteries SIB and lithium-ion batteries (LIB) through AI-driven optimization.

Parcours

Master (MESC program, 2022-2024)

---

Compétences

Projets en cours

HIPOBAT

---