Design and development of adaptive EV charging management for urban traffic environments

Al Ja'idi, Mohammad H. A. (2022) Design and development of adaptive EV charging management for urban traffic environments. Doctoral thesis, Northumbria University.

Text (Doctoral Thesis)
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Due to the world’s shortage of fossil fuels, increasing energy demand, oil prices, environmental concerns such as climate change and air pollution, seeking for alternative energy has emerged as a critical study area. Transportation systems is one of the main contributors to air pollution and consumers of energy. Electric Vehicles (EVs) is considered as a highly desirable solution for a new sustainable transportation for many powerful advantages, such as energy efficient, environmentally friendly and may benefit from increased renewable energy technologies in the future. Despite all the acknowledged advantages and recent developments in terms of reducing the environmental impact, noise reduction and energy efficiency, the electric mobility market is still below the expectations. Among the most challenges that limit the market penetration of EVs as well as achieving a sustainable mobility system are the efficient distribution of adequate Charging Stations (CSs) and also determining the best CSs for EVs in metropolitan environments.

This thesis is concerned in determining the optimal placement of EVCSs and the efficient assignment of EVs to CSs. To accomplish this, we thoroughly examine the interactions between EVs, CSs, and Electrical Grids (EGs). First, a novel energy efficient scheme to find the optimal placement of EVCSs are presented, based on minimizing the energy consumption of EVs to reach CSs. We then propose a comprehensive approach to find the optimal assignment of EVs to CSs based on optimization of EV users’ QoE. Finally, we proposed a reinforcement learning-based assignment scheme for EVs to CSs in urban areas, aiming at minimizing the total cost of charging EVs and reduce the overload on EGs. By comparing the obtained results of the proposed approaches with different scenarios and algorithms, it was concluded that the presented approaches in this thesis are effective in solving the problems of EVCS placement and EVs assignment.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: EV charging station placement, EV energy consumption model, reinforcement learning-based assignment of EVs, QoE-based assignment of EVs to charging stations, charging connector rated power
Subjects: H600 Electronic and Electrical Engineering
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
University Services > Graduate School > Doctor of Philosophy
Depositing User: John Coen
Date Deposited: 30 May 2022 08:15
Last Modified: 30 May 2022 08:15

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