A Complete Machine Learning Approach for Predicting Lithium-Ion Cell Combustion

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" A Complete Machine Learning Approach for Predicting Lithium-Ion Cell Combustion "
Almagro Yravedra, Fernando Li, Zuyi

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1054281
Doc. No	:	TL53398
Main Entry	:	Almagro Yravedra, Fernando
Title & Author	:	A Complete Machine Learning Approach for Predicting Lithium-Ion Cell Combustion\ Almagro Yravedra, FernandoLi, Zuyi
College	:	Illinois Institute of Technology
Date	:	2020
Degree	:	M.S.
student score	:	2020
Note	:	139 p.
Abstract	:	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.
Descriptor	:	Artificial intelligence
	:	Electrical engineering
Added Entry	:	Li, Zuyi
Added Entry	:	Illinois Institute of Technology