Optimal Energy Management Strategies for Electric Vehicle Charging Stations in Power Distribution Systems

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Optimal Energy Management Strategies for Electric Vehicle Charging Stations in Power Distribution Systems

ارائه دهنده: Provider: mehdi mirzadeh

اساتید راهنما: Supervisors: Dr. Alireza Hatami & Dr. Mohammad Mehdi Shahbazi

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Dr. Mohammad Hasan Moradi & Dr. Majid Ghani Zarch

زمان و تاریخ ارائه: Time and date of presentation: 8/10/2022

مکان ارائه: Place of presentation: seminar room 2 bargh group

چکیده: Abstract: Due to some of the mentioned limitations of batteries in power supply of electric vehicles such as 1- nominal capacity loss in the long run, 2- high temperature during charging and also 3- inability to meet the immediate power demand, a capacitor super in Will be used to overcome this limitation. A supercapacitor has different properties compared to a battery. In terms of power supply, it has a higher rating and can respond to load demand effectively and quickly, and they operate reasonably in a wider temperature range (from 40 to 70 degrees). Also, the internal resistance of supercapacitors is less than batteries, which reduces temperature loss and increases efficiency in transmitting instantaneous energy. In addition to the above, its life cycle (nominal capacity) is equal to half a million times (with energy storage in an electrostatic field). While in batteries this figure eventually reaches several thousand cycles. However, due to the fact that supercapacitors have lower energy density than batteries, the problem with supercapacitors is the inability to supply power over long distances. In this context, the main idea in the proposed method is to combine the two energies to overcome the shortcomings of the battery and supercapacitor, as well as to achieve better performance, because supercapacitors are a good option to fill the peak power demand gap (especially when the battery output power Be inefficient and weak). Given that working with this hybrid system (battery and supercapacitor) is not an easy task, and also to coordinate the battery and supercapacitor to meet peak power demand (albeit with low energy loss and increase the life of the hybrid system), several energy management strategies Is presented in the background section. In our proposed strategy, the power demand is supplied by the battery in the case of low oscillation or frequency and by the supercapacitor when it has a high frequency component. In this strategy, the peak battery power is eliminated when the battery power fluctuations decrease. First, the wave converter is used to offline the load power demand into two different frequency components (based on the alternating bandwidth of the battery and the supercapacitor). The neural network model, which is subsequently developed and obtained with the help of frequency data, is taught from the previous stage. The inputs of the neural network model are the load power demand and its low frequency component, the output of the power demand model is what the battery must supply. In this way, the neural network can predict battery power demand by adapting to fluctuations in different driving cycles. Finally, a fuzzy logic monitoring controller is designed in this strategy to manage the supercapacitor voltage. This dissertation also presents a new method for locating and determining the capacity of renewable resources and charging stations of electric vehicles and managing the process of charging vehicles in the network at the same time.

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