Transformation towards Electromobility and Industrial Sustainability Stellantis
The project focuses on generating knowledge around the reconfiguration of manufacturing processes for electric vehicles. The research will focus on integrating process modelling methods with predictive and prescriptive systems based on artificial intelligence.
The new model will be able to learn both from the past and from the near future, by integrating external data on possible changes in demand or disruptions in the supply of components that may affect process reconfiguration.
The result of the research will be the definition of the next generation of rapidly reconfigurable electric vehicle manufacturing systems.
The specific objectives are to:
- Build a dynamic simulation model that represents the manufacturing conditions of the real process and makes it possible to evaluate possible alternatives for electric vehicle manufacturing.
- Research a data integration model to connect the dynamic simulation models with the current real-time status of the plant processes.
- Harness the potential of artificial intelligence to gain knowledge from historical data on different manufacturing situations in the past, in order to learn from them and avoid repeating actions that may harm efficiency or the consumption of resources and energy.
- Design an advanced virtual scenario generation module to explore new combinations of actions in manufacturing processes that can be evaluated with the dynamic models.
- Research the development of an advanced interface that allows operators to interpret large amounts of data quickly, so they can make agile decisions about the manufacturing process based on the analyses and scenarios evaluated in the digital twin.
The TESIS project is funded by the Spanish Ministry of Industry, Trade and Tourism and by the European Union “NextGenerationEU” with MRR funds from the PRTR, project reference VEC-020100-2022-71. PRTR stands for the Recovery, Transformation and Resilience Plan.
