عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
NATIONAL BIBLIOGRAPHY NUMBER
Number
TLpq2446477230
LANGUAGE OF THE ITEM
.Language of Text, Soundtrack etc
انگلیسی
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
A Complete Machine Learning Approach for Predicting Lithium-Ion Cell Combustion
General Material Designation
[Thesis]
First Statement of Responsibility
Almagro Yravedra, Fernando
Subsequent Statement of Responsibility
Li, Zuyi
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
Illinois Institute of Technology
Date of Publication, Distribution, etc.
2020
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
139
DISSERTATION (THESIS) NOTE
Dissertation or thesis details and type of degree
M.S.
Body granting the degree
Illinois Institute of Technology
Text preceding or following the note
2020
SUMMARY OR ABSTRACT
Text of Note
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.
TOPICAL NAME USED AS SUBJECT
Artificial intelligence
Electrical engineering
PERSONAL NAME - PRIMARY RESPONSIBILITY
Almagro Yravedra, Fernando
Li, Zuyi
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب p
[Thesis]
276903
a
Y
