CIFRE PhD offer: Analysis and modeling of the ageing of Li ion batteries using AI F/H
EDF
Détail de l’offreInformations généralesRéférence 2024-112759Date de début de diffusion 04/07/2024Date de modification 04/07/2024Description du posteFamille professionnelle / MétierELECTRICITE COURANTS FORTS – Ingénierie / Expertise / RechercheIntitulé du posteCIFRE PhD offer: Analysis and modeling of the ageing of Li ion batteries using AI F/HType de contratThèseDescription de la missionContext
In the context of the energy transition, battery storage systems are playing an increasingly important role for both electric mobility and stationary applications. The EDF Group has positioned itself as a major player both in France and worldwide in this field, thanks to numerous storage projects installed and operated by the EDF Group.The subject of ageing of Li ion batteries is a major technological issue on which EDF’s R&D has been working for many years. This work is based in particular on a rich history of experimental data collected on dozens of distinct commercial cell references for a total of nearly 15,000 individual tests carried out in EDF’s R&D laboratories. These time series data are centralized on a datalake from which data processing can be carried out with tools adapted to the big data context. Today these data are, among other things, used to build analytical ageing models, but these models only exploit a small portion of the collected data today.Objectives
The objective of this thesis is to strengthen both our understanding of the ageing of Li ion batteries and our ability to model it by using artificial intelligence. In particular, this thesis sets itself the double objective of using machine learning approaches in order to:1. Exploit all the diversity of the experimental database to highlight new links between causes and manifestations of ageing.2. Design and test new AI-based ageing models that would bring an advantage compared to classical analytical models. This could for example correspond to the development of light, embeddable and adaptive models, updating their parameters continuously, as new data arrives. One of the final objectives is to optimize the performance and lifespan of batteries according to specific usage conditions.
Expérience minimum souhaitéeDébutantCompétences transverses
LanguesAnglais (C1 – Utilisateur expérimenté)
Seine-et-Marne
Fri, 05 Jul 2024 04:01:39 GMT
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