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M3ES

Data mining for fire classification and prediction from LIB systems

keywords CLASSIFICATION ALGORITHMS, DATA MINING, FIRE RISK, LITHIUM-IONS BATTERIES , DYNAMICS, AGEING, FATIGUE

Reference persons DAVIDE PAPURELLO

Research Groups Energy Center Lab, M3ES

Thesis type SIMULATION

Description The increasing use of LIB systems requires a critical and detailed investigation of fire scenarios. The approach using data mining techniques aims to classify the various fire scenarios, considering experimental studies, with the ultimate goal of being able to predict the heat release rate (HRR).
It is also possible, if the work is of good quality, to proceed to scientific publication.

Required skills Critical analysis and approach to the use of data mining techniques, basic knowledge of matlab


Deadline 22/03/2024      PROPONI LA TUA CANDIDATURA




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