creator
Almagro Yravedra, Fernando
A Complete Machine Learning Approach for Predicting Lithium-Ion Cell Combustion
2020
Summer 2020
Thesis
Illinois Institute of Technology
ECE / Electrical and Computer Engineering
advisor
Li, Zuyi
Electrical engineering
Artificial intelligence
Electrical Engineering
Lithium-Ion cell
Machine Learning
Neural Networks
Predictive Model
Recurrent Neural Networks
en
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.
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http://rightsstatements.org/page/InC/1.0/
Restricted Access
http://hdl.handle.net/10560/islandora:1010171