After decades of research and more than twenty years of market development, Lithium Ion Batteries (LIBs) have contributed to the commercial success of portable electronics and constitute nowadays the most used battery type in modern Electric Vehicles (EV). However, regarding EV applications, LIBs have reached a point where optimization of their electrode mesostructure is vital to decrease their cost and to achieve even higher gravimetric and volumetric energy densities. The porous electrode mesostructure and its associated properties (e.g. porosity, tortuosities, electronic conductivity) strongly depend on the adopted fabrication process and parameters, such as the slurry composition, the solvent evaporation rate and the calendering pressure. Enabling precise control of material arrangement and distribution in the electrodes is of paramount importance for designing the next generation of rechargeable battery electrodes.
The ARTISTIC Project, granted by the European Research Council, aims at developing a digital twin of LIB manufacturing process by combining physical models, Artificial Intelligence algorithms and experimental characterizations. Such a digital twin aims at predicting the influence of the process parameters on the final LIB performance upon cycling, but also at performing reverse engineering.
This "Battery Manufacturing Webinar Series" aims to develop a forum of discussion about the links between battery manufacturing process and electrode properties, encompassing both modeling and experimental approaches. Free of charge, this Webinar Series is devoted to battery scientists and engineers as well as to students interested in the energy field. The schedule combine talks given by the ARTISTIC project members and invited talks given by active battery scientists from Europe and North America. In this occasion, the ARTISTIC project members will present their latest works and will introduce the first version of the "Data Explorer", an interactive Web App which will allow users to have access and reuse the project database. The invited speakers will cover a wide diversity of linked topics, such as battery aqueous processing, autonomous discovery of materials, electrode microstructure-properties links, machine learning generation of electrode microstructures, impedance characterization of porous electrodes and much more !
Webinar connexion details will be communicated to the registrants later by email.
Please register before June, 26th (see below register form).
- Prof. Alejandro A. Franco (ARTISTIC, UPJV)ARTISTIC Project: Towards a Digital Twin of Lithium Ion Batteries Manufacturing.
- Aasutosh Mistry (ANL, USA)"Misfits" in Porous Li-ion Electrodes: Secondary Solids and Inhomogeneities.
- Teo Lombardo (ARTISTIC, UPJV) Insight in LIB Slurry through Physical-Based Modelling and Machine Learning: A Twofold Approach.
- Elixabete Ayerbe (CIDETEC, Spain)TBA
- Dr. Alain Ndganjong (ARTISTIC, UPJV)TBA
- Ivano Castelli (DTU, Denmark)How to autonomously design better battery materials and interfaces using Density Functional Theory.
- Dr. Emiliano Primo (ARTISTIC, UPJV)TBA
- Dr. Sam Cooper (Imperial College, UK)Pores for thought: Characterisation and design of battery electrode microstructures using simulation and machine learning.
- Mehdi Chouchane (ARTISTIC, UPJV)3D-Li Ion Battery Modeling thanks to an In house Meshing App.
- Antony Forner-Cuenca (TUe, The Netherlands)Towards Bottom-up Engineered Electrode Microstructures for Redox Flow Batteries.
- Abbos Shodiev (ARTISTIC, UPJV)4D-resolved physical model for Electrochemical Impedance Spectroscopy in symmetric cells.
- Prof. Miran Gaberscek (NIC, Slovenia)Impedance Spectroscopy of Battery Porous Electrodes.
- Arnaud Demortière (ARTISTIC, UPJV)3D investigation of battery electrode architecture using X-ray nano-CT and image processing with deep learning.
- Jianlin Li (ORNL, USA)Advanced processing for lithium-ion batteries.
- Bernard Lestriez (IMN, France) Influence of the electrode microstructure on charge transport and electrochemical performance by coupling nanotomography and numerical simulations.
- Marc Duquesnoy (ARTISTIC, UPJV)ARTISTIC Project Data Explorer Web App