A Complete Machine Learning Approach for Predicting Lithium-Ion Cell Combustion Almagro Yravedra, Fernando Electrical engineering Artificial intelligence Electrical Engineering Lithium-Ion cell Machine Learning Neural Networks Predictive Model Recurrent Neural Networks The object of the herein thesis work document is to develop a functional predictive model, able to predict the combustion of a US18650 Sony Lithium-Ion cell given its current and previous states. In order to build the model, a realistic electro-thermal model of the cell under study is developed in Matlab Simulink, being used to recreate the cell's behavior under a set of real operating conditions. The data generated by the electro-thermal model is used to train a recurrent neural network, which returns the chance of future combustion of the US18650 Sony Lithium-Ion cell. Independently obtained data is used to test and validate the developed recurrent neural network using advanced metrics. Li, Zuyi 2020 Thesis application/pdf islandora:1010171 http://hdl.handle.net/10560/islandora:1010171 Illinois Institute of Technology ECE / Electrical and Computer Engineering en In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access