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EDLB Ecole de la batterie

  • Partners: Verkor (Leader), AFPA, CEA INSTN, CESI, CMQe Smrt Energy Systems Campus, CORYS, EBA250, CMQe Auto’Mobilités, Grenoble INP, IMT CCI Grenoble, Cnam, Tenerrdis, Pôle Formation Isère, IUT Lyon 1.
  • Call for projects: France 2030 AMI Skills and Jobs of the Future Season 1
  • Project budget: €5.9 million
  • CESI budget for the project: €481,000
  • Project launch: November 2022
  • Project duration: 60 months

The Battery School is a networked school. The project is the result of coordination between 16 players specializing in the technological and industrial challenges of the future, including companies in the battery ecosystem, competitiveness clusters, laboratories, and training organizations at the national level.

The four main objectives of the school are:

  • Consolidate existing resources by validating and developing a map of companies’ needs and training gaps.
  • Train trainers by creating a network of experts and knowledge centers in the field of batteries to enable the transfer of cutting-edge knowledge.
  • Create specific educational pathways for the battery industry, in direct collaboration with manufacturers and their regional and national needs.
  • Attract talent to work in the battery industry, from young people to employees and those undergoing retraining.

The training programs include innovative teaching methods based on inter-level workshops, mentoring, and the use of digital technology, including digital twins, dynamic simulations, and virtual reality.

The project aims to train 1,600 people each year in production, engineering, industrial engineering, and design offices in the fields of electrochemistry, thermal engineering, mechanics, and team management. By 2030, at least 8,000 people will be trained in battery-related professions, which corresponds to the minimum local requirements.

Actions completed in 2023:

  • Design of training content for the “Electrical Energy Storage” option for CESI general engineers.
  • Launch of Sarah OUARAB’s doctoral thesis entitled “Characterization of the affordances of a Cyber-Physical Production System, based on multimodal perception of human-system interactions.””.

Actions completed in 2024:

  • As part of Sarah Ouarab’s thesis, the work carried out between March 2024 and March 2025 focused mainly on one of the major scientific challenges: detecting the elements of an industrial work environment (workstation) used by an operator when performing manufacturing tasks. The approach developed relies on deep learning techniques to train a detection model using a mixed dataset, combining virtual data generated from the digital twin of the workstation and real data collected during experiments. The objective was to identify the minimum amount of real data to be included in a mixed dataset in order to limit the effort required to collect and annotate real data, while ensuring a level of detection performance compatible with the requirements of the targeted application.