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Design and Development of Adaptive EV Charging Management for Urban Traffic Environments.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Design and Development of Adaptive EV Charging Management for Urban Traffic Environments./
作者:	Al Ja'idi, Mohammad H. A.
面頁冊數:	1 online resource (238 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: A.
Contained By:	Dissertations Abstracts International84-03A.
標題:	User behavior. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29282671click for full text (PQDT)
ISBN:	9798845434616

Design and Development of Adaptive EV Charging Management for Urban Traffic Environments.
Al Ja'idi, Mohammad H. A.

Design and Development of Adaptive EV Charging Management for Urban Traffic Environments. - 1 online resource (238 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: A.

Thesis (Ph.D.)--University of Northumbria at Newcastle (United Kingdom), 2022.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798845434616Subjects--Topical Terms:

3564661
User behavior.
Index Terms--Genre/Form:

542853
Electronic books.

Design and Development of Adaptive EV Charging Management for Urban Traffic Environments.
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520 $a 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.
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