{"id":27433,"date":"2024-02-21T10:58:06","date_gmt":"2024-02-21T09:58:06","guid":{"rendered":"https:\/\/www.ita.es\/proyecto\/thesis-perte-vec\/"},"modified":"2024-02-21T10:58:30","modified_gmt":"2024-02-21T09:58:30","slug":"thesis-perte-vec","status":"publish","type":"proyecto","link":"https:\/\/www.ita.es\/en\/project\/thesis-perte-vec\/","title":{"rendered":"THESIS PERTE VEC"},"content":{"rendered":"\n

TITLE OF THE PROJECT:<\/h2>\n\n

T.E.S.S.I.S – Transformation towards Electromobility and Industrial Sustainability Stellantis<\/p>\n\n

GENERAL DATA:<\/h2>\n\n

Program: Ministry of Industry, Trade and Tourism Strategic Project for Economic Recovery and Transformation in the Electric and Connected Vehicle Sector (PERTE VEC), within the framework of the Recovery, Transformation and Resilience Plan.Reference: VEC-020100-2022-71Date of RESOLUTION: 20\/01\/2023Execution date: 15\/05\/2022 – 30\/06\/2025<\/p>\n\n

PRESENTATION AND OBJECTIVES:<\/h2>\n\n

The objective of the project focuses on the generation of knowledge about the reconfiguration of manufacturing processes for electric vehicles. The research will focus on the integration of process modeling methods with artificial intelligence-based predictive and prescriptive systems. The new model will have the ability to learn from both the past and the near future through the integration of external data on possible changes in demand or disruptions in component supply that may affect process reconfiguration. The result of the research will be the definition of the next generation of rapidly reconfigurable electric vehicle manufacturing manufacturing systems.<\/p>\n\n

The specific objectives are as follows:<\/p>\n\n

– To build a dynamic simulation model to represent the manufacturing conditions of the real process and to evaluate possible alternatives for electric vehicle manufacturing.– Investigate a data integration model to connect the dynamic simulation models with the actual real-time situation of the plant processes.– Leverage the potential of artificial intelligence to help gain insight from historical data of different manufacturing casuistries that have occurred in the past, in order to learn from them and not repeat actions that may be detrimental to efficiency or resource and energy consumption.– 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 on the development of an advanced interface that allows operators to interpret a large amount of data quickly in order to be able to make decisions about the manufacturing process in an agile way from the analyses and scenarios evaluated in the digital twin.<\/p>\n\n

PARTICIPATING ENTITIES:<\/h2>\n\n

INSTITUTO TECNOLOGICO DE ARAGONOPEL ESPA\u00d1A, S.L.U.<\/p>\n\n

BUDGET:<\/h2>\n\n

TOTAL BUDGET: 1.489.726,80 \u20ac.TOTAL BUDGET ITAINNOVA: 511.084,60 \u20ac.<\/p>\n\n

FINANCING:<\/h2>\n\n

TOTAL FINANCING: 744.863,00\u20ac.TOTAL FINANCING ITANNOVA: 255.542,00 \u20ac.<\/p>\n\n

TESIS project is financed by the Ministry of Industry, Trade and Tourism and by the European Union “Next GeneratinEU” with PRTR MRR funds, being project reference VEC-020100-2022-71. PRTR the acronym for Recovery, Transformation and Resiliency Plan.<\/p>\n\n

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