magistrsko delo
Primož Kocuvan (Author), Rastko Fišer (Mentor)

Abstract

V sklopu pričujočega dela izdelamo simulacijski model generičnega električnega vozila, s katerim virtualno vozimo po naključno kreiranih cestah z različnimi na- klonskimi profili in hitrostmi vozila. Nato podatke o vožnji uporabimo za strojno učenje učnih modelov, kjer z računanjem optimiziramo izkoristek vožnje za po- ljubno pot. Delo je sestavljeno iz 7 sklopov. V prvem sklopu opišemo vozno- merilne cikle kot sta NEDC in WLTP ter kreiranje naključnih hitrostnih profilov, ki so hibrid med NEDC in WLTP. V drugem sklopu opišemo ravnovesje sil, ki vplivajo na osebno vozilo, tisto pogonsko ter ostale, ki delujejo v nasprotno smer. V tretjem sklopu opišemo model baterije ter njegovo implementacijo v Simulinku. Tukaj vključimo nekaj kronološko pomembnih podatkov ter uporabljene tehno- logije za izdelavo baterij. Najbolj obsežno poglavje oziroma sklop je četrti, kjer predstavimo sinhronski motor, ki je v večji meri predstavljen z vpoglednimi tabe- lami, ki so bile predhodno izračunane z metodo končnih elementov in definirajo lastnosti pogonskega motorja v posameznih obratovalnih točkah. Vključimo tudi ustrezne grafe simulacijskih rezultatov. V petem sklopu opišemo namen regulacije ali krmiljenja motorja ter PI regulator, ki je uporabljen v našem modelu vozila za regulacijo navora, kot vhod pa dobi dejansko hitrost in referenčno hitrost vozila. V šestem sklopu opišemo algoritme strojnega učenja in kreirane učne modele, kot so nevronska mreža, metoda podpornih vektorjev in k-najbližjih sosedov. V za- dnjem, sedmem sklopu analiziramo in ovrednotimo rezultate ter opišemo možno nadaljnje delo.

Keywords

WLTP cikel;fizikalno-matematični modeli električnega vozila;strojno učenje;sinhronski motorji;IPM motor;magisteriji;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FE - Faculty of Electrical Engineering
Publisher: [P. Kocuvan]
UDC: 621.31(043.3)
COBISS: 86219779 Link will open in a new window
Views: 207
Downloads: 42
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: English
Secondary title: Selection of electrical vehicle's motor power applying machine learning methods
Secondary abstract: As part of the present work, we create a simulation model of a generic electric vehicle with which we virtually drive on randomly created roads, with different inclination profiles and vehicle speeds. Later, we use the data of the ride itself to train learning models that optimize the calculation of route efficiency for any route. The work consists of 7 chapters. In the first part, we describe driving- measuring cycles such as NEDC and WLTP and how to create a random speed profile for the needs of the master’s thesis, which profile is a hybrid between NEDC and WLTP. In the second chapter, we describe the balance of forces on the road, ie the forces that affect the personal vehicle, the propulsion, and others that act in the opposite direction. In the third part, we describe the battery model and its implementation in Simulink. Here we include some chronologically important data and the technologies used to make the batteries. The most extensive chapter or section is the fourth, where we present a synchronous motor, which is largely represented by look-up tables. We also include the corresponding graphs of the simulation results. In the fifth chapter, we describe the purpose of control, and the PI regulator used in our vehicle model for torque control, where the input of the controller gets the actual speed and reference speed. In the sixth chapter, we describe machine learning algorithms and create learning models such as neural network, support vector machine, and k-nearest neighbors. In the last seventh chapter, we analyze the results and describe further work.
Secondary keywords: WLTP cycle;mathematical model of electric vehicle;machine learning;synhronous motor;IPM motor;
Type (COBISS): Master's thesis/paper
Study programme: 1000316
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za elektrotehniko
Pages: XXII, 107 str.
ID: 13990475