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dc.contributor.authorEROĞLU, Hasan
dc.contributor.authorOĞUZ, Yasin
dc.contributor.authorKAPLAN, Emrah
dc.contributor.authorGÜL, Fatih
dc.contributor.authorŞİMŞEK, Cemaleddin
dc.description.abstractThe efficiency of electric vehicles is becoming more and more important every day. Accurate determination of the speed values that the electric vehicle driver has to go at different slopes of the road route enables the electrical vehicle to be used more efficiently. Inefficient use of vehicles with different slope values of the road compared to engine characteristics increases energy consumption. There are many algorithms used for optimizing the solution. Genetic algorithm which produces effective results by using crossover and mutation operators is widely used in the literature. In this study, with the help of genetic algorithm, it was tried to find the optimum speed values which can provide reaching to the target in the desired time by consuming the least amount of energy in the road routes with different slope values for electric vehicles. For this purpose, Hub engine consumption characteristic which is widely used in electric vehicles is transferred to software environment. In addition, a sample route of approximately 2 km in length is determined and the coordinates of the route and the slope values in the ArcGIS software environment of Geographic Information Systems are processed in different layers and transferred to the software environment. A software that uses the Genetic Algorithm functions by reading the specified road and motor characteristics from two different text files and providing the user with the best results from the alternatives has been realized with the help of C# programming language in Visual Studio environment. The developed software will provide the basis for future vehicle efficiency studies, while allowing electric vehicles to go longer.en_US
dc.subjectElectric vehiclesen_US
dc.subjectdriving optimizationen_US
dc.subjectgenetic algorithmen_US
dc.titleIncreasing the Electrical Vehicle Efficiency with Genetic Algorithmen_US
dc.relation.publicationcategoryUluslararası Yayınen_US

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