When there are fire compartments, the decision-making of emergency response are mainly based on human decisions, the result of experience and voluntarism, without a full understanding of its evolution over time. In the fight against the fires, knowledge of their dynamics and their hazards and the ability to measure, communicate and interpret the variables that characterize constitute essential elements in assessing the situation to organize optimize and secure emergency. This is the challenge of this project whose objectives are to determine by experimental and numerical specific laws governing phenomena compartment fires and in a second phase approach from these laws, migrating systems current liabilities to safety and intelligent communication systems , optimizing and securing the human and organizational emergency response.

The upcoming growth of the electric vehicles market to accompany profound societal and ecological mutations requires a large-scale deployment of electric charging points. The first milestone of 100,000 public charging points having almost been reached, the next objective of the French government is 500,000 charging points by 2027. This growth incurs new impactful cyber physical security risks for power grid, transportation infrastructures, customers, and operating companies. Therefore, a need to detect and identify these risks is crucial to offer secure and trustworthy charging units for electric vehicles. Our project aims at designing robust data-driven AI algorithms to detect abnormal events and behaviors on charging points. Ultimately, the elaborated algorithms, accompanied with cyber behavioral analysis module and potential attacks’ mitigation actions, will be deployed in production on real charging points.