20.500.12556/DKUM-75132 Določanje napolnjenosti baterij z uporabo umetnega nevronskega omrežja magistrsko delo State of charge estimation for batteries using artificial neural network 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. 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. umetna nevronska omrežja določanje stanja napolnjenosti baterij litij-ionske baterije LTO artificial neural network state of charge estimation lithium-ion battery LTO true true false [R. Rečnik] Slovenski jezik Angleški jezik Magistrsko delo/naloga 2019-09-24 00:19:55 2019-11-21 09:01:23 2022-08-09 10:00:58 2019-12-14 03:17:53 2019 0 Maribor 0 Maribor XVIII, 135 str. 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.354.32.08(043.2) 22774550 URN:SI:UM:DK:8PMLBH1C MAG_Recnik_Rok_2019.pdf MAG_Recnik_Rok_2019.pdf 1 EF685CFDE9D083CA95079FF4D794138A ac44381c73e07f6508b22a8f6cc252df655e101db698a4d03d5ba59354bb5808 2b8d00e2-7c11-11eb-bb7a-00155d0001ca 20.500.12556/dkum/f4e3b5ad-62be-425a-a7a7-f196c7529009 https://dk.um.si/Dokument.php?lang=slv&id=140004 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0