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Modeling: from materials to electrochemical systems

Modeling: from materials to electrochemical systems


Coordinator: Prof. Alejandro Franco

Staff involved: E. Baudrin, C. Delacourt, A. FrancoC. Frayret, A. Demortière, M. Morcrette


Research activities on this crosscutting theme focuses on mathematical modelling and numerical simulation of electrochemical systems for Energy storage and conversion, from material to device scale: lithium ion, lithium sulfur, metal air and redox batteries, fuel cells… Those activities aim to support and guide experimenters and industrials, seeking for more effective electrochemical systems, but also trying to understand, interpret and predict the mechanisms involved in those complicated systems.

Modelling work consist in the physical problem formulation, mathematical equation and IT programming to solve equations. This work is closely developed with experimental characterization to identity some physicochemical quantities which are used as models’ input parameters. Those characterization may carry on the study of prototypes, commercial items (for ex. Commercial Li-ion cells) or on simplified « model » systems, to focus on a well-defined physicochemical mechanism to study it extensively (i.e. model experiment). The theme take also a multiscale modelling approach, to determine parameters from theoretical calculation realized at atomic and molecular scale, which are injected in higher scale models afterwards.

The theme is driven by researchers with complementary expertise, which granted LRCS from a wide range of simulation methods, some of them pioneer on an international level, and mostly located in internally developed software. For example : Density Functional Theory (DFT), course-grained molecular dynamics, Monte Carlo kinetic method, methods called « de champs de phases », continuum type models and hybrid multiscale type. Researchers are developing artificial intelligence algorithm to automate models’ parametrization and to carry out data mining, and also virtual reality software’s to visualize and interact in an immersive way with calculations’ data and with « serious games » for teaching.


The research activities are about:


Multi-scale modelling of Li-ion cells manufacture process

This Activity is around the « ARTISTIC » ERC PROJECT lead by Professor A.A. Franco (, the aim is to create a multiscale computational platform predictive of the impact of the manufacture parameters on the Lithium Ion batteries’ electrode texture and its electrochemical response. Artificial intelligence’s algorithm are also developed to easily configure models and to predict the best formulation reaching targeted batteries’ performances.



Material reactivity and electrochemical interfaces’ modelling

Physicochemical models are applied to the materials and interface reactivity study within energy storage and conversion. For instance, approaches on mesoscopic scale have been developed to study the lithiation process in bi-phasic multi-particles systems and the electro-catalyzer reactivity in fuel cells.



Study and modelling of the ageing of Li-ion cells

Ageing of Li-ion batteries is studied from an experimental point of view and thanks to the physicochemical modelling tools developed in the LRCS. While ageing commercial graphite/LFP batteries is only leading to an internal balancing change because of side reactions such as SEI continuous formation, the cycling ageing brings those batteries up to a loss of active material in the negative electrodes.   An ageing mathematical model, including all phenomena experimentally noted, had been set up and can be used to predict the lifetime.




Multi-scale modelling of lithium sulfur batteries

We are developing physicochemical models with several spatial-temporal scale for a better understanding of Lithium Sulfur batteries working principle and to predict electrodes design and the best operational conditions. Those models are describing electrochemical mechanism leading to the polysulfide’s formation and to Li2S within positive electrodes and allow to study their impact on the transport properties during batteries charge and discharge.

Multi-scale modelling of metal air batteries

We are developing physicochemical models describing electrochemical and transport mechanisms within metal air batteries’ electrodes. The team is interested in the nucleation modelling and in Li2O2 particles growth within Lithium air batteries positive electrode during discharge as well as the mechanism leading to the decomposition throughout batteries’ recharge. The Impact of location and interconnection between pores on those devices global performance is also studied.

Redox flow batteries modelling

This activity focuses on the study of the redox flow batteries electrochemical response. A series of Monte Carlo Kinetic and DPD (Dissipative Particle Dynamics) models with a 3D spatial resolution has been developed and are used to analyze the impact of cycling conditions on percolation progress between suspension’s active and conductive particles.

Artificial intelligence and virtual reality applied to batteries

Deep Learning algorithms are developed to help mathematical models parametrization (force fields in molecular dynamics) and for text and data mining. We are also developing software in virtual reality to visualize and interact on an immersive way with materials, electrodes and batteries systems, to help understand their tridimensional structure and texture, to make accessible simulation tools to non-experts and to set up ludic teaching methods.

Model Experiments

Experiments, called « model » are developed to focus on a specific physicochemical phenomenon:

- for a better understanding
- to offer the most suitable physical representation and the resultant mathematical description
- to identify its physical/geometrical parameters

Some model experiments are implemented for the SEI growth on the Li-ion batteries’ negatives electrodes, the study of the porous electrodes key parameters such as tortuosity, or transport properties for Li-ion batteries with carbonate based electrolytes.