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Artificial Intelligence to Accelerate the Manufacturing and Design of Rechargeable Batteries H/F

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General information

Reference : UMR7314-ALEFRA-010
Workplace : AMIENS
Date of publication : Monday, June 22, 2020
Scientific Responsible name : Alejandro A. Franco
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 October 2020
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

Context: Optimizing the manufacture of lithium ion batteries and the design of cells remains a difficult multivariable task, subject to constraints such as the desired end performance, availability of raw materials, cost, safety, environmental impact and recyclability. The roadmap of the large-scale European initiative Battery 2030+ highlights these challenges and proposes research actions which aim to revolutionize the way we design batteries today. Among them, the use of artificial intelligence (AI) tools to perform the reverse design of material interfaces is recognized as of paramount importance.

State of the art: The optimization of the battery cells relies heavily on an empirical approach of trial and error type which consists first of all in manufacturing the electrodes, then in assembling them in a cell and finally in characterizing their performance. Such an approach is inefficient in terms of time and cost, which underlines the need for new methodologies to accelerate the R&D of lithium ion batteries. AI is starting to be used in the field of batteries, in particular for the discovery of materials and for the prediction of battery aging. Recently, the LRCS reported for the first time an AI-based methodology, formed with experimental data, capable of predicting the charge and porosity of NMC electrodes according to their manufacturing parameters such as the viscosity of the suspension and the solid / liquid ratio.

Objective: This doctoral project aims for the first time to develop and demonstrate an innovative AI-driven platform capable of performing two complementary tasks: 1) unraveling the interdependencies between the battery manufacturing parameters and the properties of the electrodes, cell design and performance; 2) perform a reverse design, i.e. predict the necessary electrode properties (eg porosity, loading) to achieve a desired performance at the cell level, and predict the manufacturing parameters necessary to obtain electrodes with such properties.

Work Context

The work will take place in the Reactivity and Chemistry of Solids Laboratory (UMR CNRS 7314) and in the Spanish institute CIDETEC Energy Storage. The first is located in Amiens, and it specializes in research on the electrochemical storage of energy (rechargeable batteries). It employs around 100 people. CIDETEC Energy Storage is located in San Sebastian (Spain) and it also specializes in research and prototyping of rechargeable batteries.

Constraints and risks


Additional Information

Thesis co-supervised between the LRCS (UMR CNRS 7314) and CIDETEC Energy Storage (Spain). Funding: ALISTORE European Research Institute.

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