Search

Browse

• Communities
• Authors
• Year
• Titles
• Subjects

My Space

• Login

Optimal sizing and placing of electric vehicle charging stations using aerial monitoring of the traffic

Arvanitis Chrysostomos

Full record

---

Summary

Global warming has brought about significant climate change, triggering a chain reaction that threatens human existence. Vehicles with internal combustion engines emit carbon dioxide CO2, a gaseous pollutant that enhances the phenomenon, into the environment. With the rise in the world's population linked to the increase in vehicles with conventional engines, it is becoming increasingly imperative to produce vehicles that do not emit harmful pollutants. This realization coupled with the fact that oil reserves are running out has led automakers to focus on producing electric cars that come with advantages and disadvantages over those with internal combustion engines.In this thesis, a method is followed to find the optimal locations for the placement of charging stations along the Northern Road Axis of Crete (NRAC). First, to calculate the speeds of moving vehicles on the NRAC, video recordings were made with a drone and from these videos the dataset was extracted to train the pre-trained weights of YOLOv5s. The new weights were then used to detect the vehicles in the videos, combined with OpenCV to detect their motion, in Python programming language, and finally the velocities of the moving vehicles were extracted.In the next stage, an energy evaluation is performed to estimate the power consumption of the vehicle battery according to their dynamic equations. The number of moving vehicles for each hour of the day was calculated from a survey by the Ministry of Infrastructure and Transport. The scenarios are Chania-Rethymnon, Rethymnon-Heraklion and Chania-Heraklion. The speed assigned to each vehicle is linked to the actual speeds calculated from the videos while the charging and stress rate are linked to the corresponding scenario. Finally, an inference is drawn about whether a driver is looking for a charging station along the motorway based on the battery charge rate and driver anxiety rate by a fuzzy logic system.Then, by adjusting the parameters of PSO appropriately for each scenario, the optimal locations for the placement of charging stations are calculated based on the cost function. The number of chargers is calculated from the charging time and the number of vehicles at each station. To summarize, the thesis focuses on the most efficient placement of charging stations for the above scenarios, using the fuzzy logic system, mentioned above, to estimate the mileage a driver searches for a charging station and the Particle Swarm Optimazion algorithm for optimal placement of charging stations and the number of chargers at them.

Available Files

Services