{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1944"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1944","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Optimal electric vehicle charging management: coordination of multiple charging methods and technologies","abstract":"The global electric vehicle (EV) industry continues to expand rapidly. From the power grid perspective, expanding EV adoption could adversely impact the grid if its load is left uncontrolled. EV users also deal with challenges such as high charging time and low charger availability, especially in urban areas with huge populations and various types of charging demands. This dissertation initially reviews EV charging technologies and presents a new classification. Next, to address the EV charging management challenges, it investigates optimal EV charging management models and studies the coordination of different charging methods and technologies. This work has two major parts: (i) EVs' Operation and Control Algorithm and (ii) EVs' Optimization Considering Multiple Charging Technologies. In the first part of this dissertation, a distributed optimization framework is developed based on the alternating direction method of multipliers (ADMM) as an exchange problem to solve the electric vehicle charging management problem (EVCMP). Next, the proposed framework is expanded by employing a collaboration layer between different EV aggregators (EVA) to increase the optimization's overall efficiency while preserving EVAs' independence. The proposed coordinated distributed platform (CDP) enhances the load profile's smoothness compared to the locally coordinated and uncoordinated charging platforms and decreases EV charging costs. In the second part, we introduced a multi-charger framework including both fixed and mobile charging stations for optimal operation of EVs, which covers the shortcomings of stand-alone usage of each charging technology. The proposed framework selects the best charging station type and location to minimize the users' overall charging time and cost and mitigate the stress on the electricity network caused by EV charging, especially during peak hours.","abstract_html":"The global electric vehicle (EV) industry continues to expand rapidly. From the power grid perspective, expanding EV adoption could adversely impact the grid if its load is left uncontrolled. EV users also deal with challenges such as high charging time and low charger availability, especially in urban areas with huge populations and various types of charging demands. This dissertation initially reviews EV charging technologies and presents a new classification. Next, to address the EV charging management challenges, it investigates optimal EV charging management models and studies the coordination of different charging methods and technologies. This work has two major parts: (i) EVs' Operation and Control Algorithm and (ii) EVs' Optimization Considering Multiple Charging Technologies. In the first part of this dissertation, a distributed optimization framework is developed based on the alternating direction method of multipliers (ADMM) as an exchange problem to solve the electric vehicle charging management problem (EVCMP). Next, the proposed framework is expanded by employing a collaboration layer between different EV aggregators (EVA) to increase the optimization's overall efficiency while preserving EVAs' independence. The proposed coordinated distributed platform (CDP) enhances the load profile's smoothness compared to the locally coordinated and uncoordinated charging platforms and decreases EV charging costs. In the second part, we introduced a multi-charger framework including both fixed and mobile charging stations for optimal operation of EVs, which covers the shortcomings of stand-alone usage of each charging technology. The proposed framework selects the best charging station type and location to minimize the users' overall charging time and cost and mitigate the stress on the electricity network caused by EV charging, especially during peak hours.","abstract_has_math":false,"creators":["Afshar, Shahab"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Disfani, Vahid R.","Karrar, Abdelrahman A.; Ahmed, Raga; Barati, Masoud","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-09-01T07:00:00Z","date_published":"2023-09-01T07:00:00Z","updated_at":"2026-07-24T05:47:06Z","subjects":["Battery charging stations (Electric vehicles)","Electric vehicles--Batteries","Mathematical models"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/770","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Disfani, Vahid R.","Karrar, Abdelrahman A.; Ahmed, Raga; Barati, Masoud","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Afshar, Shahab"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08-01T07:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-09-01T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral dissertations","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Battery charging stations (Electric vehicles)","Electric vehicles--Batteries","Mathematical models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/770"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Computational Science","Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy."]},{"key":"dc:description.abstract","label":"Abstract","values":["The global electric vehicle (EV) industry continues to expand rapidly. From the power grid perspective, expanding EV adoption could adversely impact the grid if its load is left uncontrolled. EV users also deal with challenges such as high charging time and low charger availability, especially in urban areas with huge populations and various types of charging demands. This dissertation initially reviews EV charging technologies and presents a new classification. Next, to address the EV charging management challenges, it investigates optimal EV charging management models and studies the coordination of different charging methods and technologies. This work has two major parts: (i) EVs' Operation and Control Algorithm and (ii) EVs' Optimization Considering Multiple Charging Technologies. In the first part of this dissertation, a distributed optimization framework is developed based on the alternating direction method of multipliers (ADMM) as an exchange problem to solve the electric vehicle charging management problem (EVCMP). Next, the proposed framework is expanded by employing a collaboration layer between different EV aggregators (EVA) to increase the optimization's overall efficiency while preserving EVAs' independence. The proposed coordinated distributed platform (CDP) enhances the load profile's smoothness compared to the locally coordinated and uncoordinated charging platforms and decreases EV charging costs. In the second part, we introduced a multi-charger framework including both fixed and mobile charging stations for optimal operation of EVs, which covers the shortcomings of stand-alone usage of each charging technology. The proposed framework selects the best charging station type and location to minimize the users' overall charging time and cost and mitigate the stress on the electricity network caused by EV charging, especially during peak hours."]},{"key":"dc:title","label":"Title","values":["Optimal electric vehicle charging management: coordination of multiple charging methods and technologies"]}]}],"canonical_facts":{"dc:contributor":["Disfani, Vahid R.","Karrar, Abdelrahman A.; Ahmed, Raga; Barati, Masoud","College of Engineering and Computer Science"],"dc:creator":["Afshar, Shahab"],"dc:date":["2022-08-01T07:00:00Z"],"dc:date.available":["2023-09-01T07:00:00Z"],"dc:description":["Dept. of Computational Science","Ph. 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In the first part of this dissertation, a distributed optimization framework is developed based on the alternating direction method of multipliers (ADMM) as an exchange problem to solve the electric vehicle charging management problem (EVCMP). Next, the proposed framework is expanded by employing a collaboration layer between different EV aggregators (EVA) to increase the optimization's overall efficiency while preserving EVAs' independence. The proposed coordinated distributed platform (CDP) enhances the load profile's smoothness compared to the locally coordinated and uncoordinated charging platforms and decreases EV charging costs. In the second part, we introduced a multi-charger framework including both fixed and mobile charging stations for optimal operation of EVs, which covers the shortcomings of stand-alone usage of each charging technology. The proposed framework selects the best charging station type and location to minimize the users' overall charging time and cost and mitigate the stress on the electricity network caused by EV charging, especially during peak hours."],"dc:identifier":["https://scholar.utc.edu/theses/770"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Battery charging stations (Electric vehicles)","Electric vehicles--Batteries","Mathematical models"],"dc:title":["Optimal electric vehicle charging management: coordination of multiple charging methods and technologies"],"dc:type":["Doctoral dissertations","Text"]},"updated_at":"2026-07-24T05:47:06Z"}