Integracija električnih vozila u energetske i transportne sustave

There is a strong tendency of development and application of different types of electric vehicles (EV). This can clearly be beneficial for transport systems in terms of making it more efficient, cleaner, and quieter, as well as for energy systems due to the grid load leveling and renewable energy sources exploitation opportunities. The latter can be achieved only through application of smart EV charging technologies that strongly rely on application of optimization methods. For the development of both EV architectures and controls and charging optimization methods, it is important to gain the knowledge about driving cycle features of a particular EV fleet. To this end, the paper presents an overview of (i) electric vehicle architectures, modeling, and control system optimization and design; (ii) experimental characterization of vehicle fleet behaviors and synthesis of representative driving cycles; and (iii) aggregate-level modeling and charging optimization for EV fleets, with emphasis on freight transport.U novije vrijeme postoji izražena težnja za razvojem i korištenjem različitih tipova električnih vozila. Ovo može biti korisno sa stanovišta transportnih sustava u smislu omogućavanja efikasnijeg, čišćeg, i tišeg transporta, kao i iz perspektive energetskih sustava zbog dodatnih potencijala za poravnanje opterećenja mreže i iskorištenje obnovljivih izvora energije. Potonje može biti ostvareno samo kroz korištenje tehnologija naprednog punjenja električnih vozila, koje se često temelje na primjeni optimizacijskih postupaka. Za razvoj prikladnih konfiguracija, upravljačkih sustava te metoda pametnog punjenja električnih vozila, potrebno je steći uvid u značajke voznih ciklusa razmatrane flote električnih vozila. Imajući u vidu navedeno, članak predstavlja pregled (i) konfiguracija i modeliranja električnih vozila, te optimiranja i sinteze njihova upravljačkog sustava; (ii) eksperimentalne karakterizacije ponašanja flote vozila i sinteze reprezentativnih voznih ciklusa; te (iii) modeliranja i optimiranja punjenja flote električnih vozila na agregatnom nivou, s naglaskom na teretni transport

Projektiranje i ispitivanje eksperimentalne magnetoreološke spojke

Owing to very good controllability, simple design, and durability, magnetorheological fluid (MRF) clutches become attractive solutions for various industrial and automotive applications. An experimental MRF clutch has been developed at the University of Zagreb, in order to support MRF clutch modeling, and control research. The clutch design facilitates MRF handling, change of fluid gap width, and testing various types of seals. The paper first presents calculation of the main clutch design parameters. Next, design of the overall clutch mechatronic system is described. Finally, the main results of testing the clutch static and transient behaviors are presented and compared with the design parameters.Zahvaljujući veoma dobrom svojstvu upravljanja, jednostavnoj konstrukciji i izdržljivosti, spojke temeljene na magnetoreološkim fluidima nalaze sve širu primjenu u industriji i tehnici motornih vozila. Eksperimentalna magnetoreološka spojka razvijena je na Sveučilištu u Zagrebu da bi se potakla istraživanja na području modeliranja i regulacije magnetoreoloških spojki. Spojka je konstruirana tako da olakša rukovanje fluidom, te omogući promjenu širine fluidnog raspora i primjenu raznih vrsta brtvi. Članak prvo izlaže proračun glavnih konstrukcijskih parametara spojke. Zatim se opisuje cjelokupni mehatronički sustav spojke. Konačno, prikazuju se glavni rezultati ispitivanja statičkog i dinamičkog ponašanja spojke, koji se uspoređuju s projektnim parametrima

Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range

A deep neural network-based approach of energy demand modeling of electric vehicles (EV) is proposed in this paper. The model-based prediction of energy demand is based on driving cycle time series used as a model input, which is properly preprocessed and transformed into 1D or 2D static maps to serve as a static input to the neural network. Several deep feedforward neural network architectures are considered for this application along with different model input formats. Two energy demand models are derived, where the first one predicts the battery state-of-charge and fuel consumption at destination for an extended range electric vehicle, and the second one predicts the vehicle all-electric range. The models are validated based on a separate test dataset when compared to the one used in neural network training, and they are compared with the traditional response surface approach to illustrate effectiveness of the method proposed. Document type: Articl