Določanje napolnjenosti baterij z uporabo umetnega nevronskega omrežja : magistrsko delo

Rečnik, Rok

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Title:	Določanje napolnjenosti baterij z uporabo umetnega nevronskega omrežja : magistrsko delo
Authors:	ID Rečnik, Rok (Author) ID Štumberger, Gorazd (Mentor) More about this mentor... ID Sukič, Primož (Comentor)
Files:	MAG_Recnik_Rok_2019.pdf (8,88 MB) MD5: EF685CFDE9D083CA95079FF4D794138A PID: 20.500.12556/dkum/f4e3b5ad-62be-425a-a7a7-f196c7529009
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	Glavni cilj magistrske naloge je določitev stanja napolnjenosti baterij s pomočjo umetnega nevronskega omrežja. Določitev stanja napolnjenosti (SOC) baterij predstavlja velik izziv, saj je SOC težko natančno določiti. V delu je bil obravnavan tip izredno zmogljivih Toshibinih litij-ionskih baterij s titanovim oksidom (LTO). Na podlagi pregledane literature je bila izbrana metoda določanja SOC z umetnim nevronskim omrežjem. S pomočjo namensko izdelanega testerja baterij so bile opravljene meritve toka, napetosti in temperature baterije. Meritve so bile izvedene s pomočjo merilnega sistema Dewesoft Sirius HS. Podatki so bili obdelani v programskem okolju Matlab, kjer se je tudi kreiralo in naučilo umetno nevronsko omrežje. Testi nevronskega omrežja so pokazali, da je sposobno napovedovanja SOC. S pomočjo programa v Simulinku so bili izvedeni testi za napovedovanje SOC v realnem času.
Keywords:	umetna nevronska omrežja, določanje stanja napolnjenosti baterij, litij-ionske baterije, LTO
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[R. Rečnik]
Year of publishing:	2019
Number of pages:	XVIII, 135 str.
PID:	20.500.12556/DKUM-75132
UDC:	621.354.32.08(043.2)
COBISS.SI-ID:	22774550
NUK URN:	URN:SI:UM:DK:8PMLBH1C
Publication date in DKUM:	21.11.2019
Views:	2195
Downloads:	378
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	24.09.2019

Secondary language

Language:	English
Title:	State of charge estimation for batteries using artificial neural network
Abstract:	The main goal of this Master's thesis is estimating the state of charge of batteries with the help of an artificial neural network. Estimating the state of charge (SOC) of batteries is a big challenge, because it is hard to estimate SOC precisely. The main focus was working with Toshiba's lithium-ion batteries that contain titanium oxide (LTO). With regards to available literature, a method for estimating SOC by the use of artificial neural networks was chosen. Battery current, voltage and temperature measurements were done with the help of a battery tester designed specifically for this purpose. The measurements were recorded using a measuring system called Dewesoft Sirius HS. The data was processed in the programming environment Matlab. Using the same programming environment a neural network was built and trained. Tests of the trained neural network showed that it was capable of assesing the SOC. Tests on determining SOC in real time were done with the help of the program Simulink.
Keywords:	artificial neural network, state of charge estimation, lithium-ion battery, LTO

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