Development, optimization and analysis of artificial intelligence based sustainable transportation model for range extention in electric vehicles

In thesis study, a new energy consumption prediction model was developed by including critical parameters of electric vehicles, road parameters and geographical parameters using real-time big data to extend the range of electric vehicles. With this model, the energy consumption prediction of electric vehicles, the engine type and power selection of the vehicle to be used, and the battery capacity selection according to the route were made in the routes where electric vehicle conversion will be made. Energy consumption prediction models were obtained using big data throughout the study. While creating these models, speed, weight, slope, outside temperature, electrical and mechanical failure, acceleration and recuperation, which are among the critical parameters of the vehicle and route, were used. The thesis study was handled in six steps. In first step, a literature review was conducted and in second step, 1 billion lines of data were collected in black box of Trambuses. In third step, a new black box was purchased within the scope of the BAP project and placed in the vehicle, data was collected, and examinations were made between old and new data. In fourth step, the MTECM energy consumption prediction model was obtained with the Linear Regression method. In fifth step, GMTECM was obtained using GPR. In last step, SHO-EBECM energy consumption estimation model was created with the SeaHorse optimization technique. The models were applied to the busiest routes in Malatya and possible energy consumption values for the routes were calculated if the company converted from fossil vehicles to electric vehicles. All developed prediction models are evaluated and presented comparatively with other consumption models in the literature in terms of error rate. Keywords: Big-Data, Electric vehicles, Regression, SeaHorse, Optimization