{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/2053"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/2053","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Optimized and energy-efficient cooperative platooning for autonomous vehicles","abstract":"The increasing need for energy in most systems has resulted in the need for efficient energy usage, particularly in vehicle-based systems. This thesis investigates energy-efficient platooning of Connected and Autonomous Vehicles(CAVs) in simulation frameworks using energy modelling. A lightweight physics-based energy model was integrated into a framework to capture kinematics data, such as aerodynamic drag and inertial dynamics in real time. This thesis focuses on evaluating multiple control strategies for energy efficiency under controlled scenarios. Three controllers were evaluated: Adaptive Cruise Control (ACC), Cooperative Adaptive Cruise Control(CACC), and proposed energy-aware controller. The energy-aware controller provided greater energy savings than the other two controllers with savings over 20% along with strongest string stability. This thesis provides insights into energy benefits that can be gained through energy-aware controllers working with information sharing in autonomous platooning, allowing for deeper insights relevant to Canadian transportation decarbonization.","abstract_html":"The increasing need for energy in most systems has resulted in the need for efficient energy usage, particularly in vehicle-based systems. This thesis investigates energy-efficient platooning of Connected and Autonomous Vehicles(CAVs) in simulation frameworks using energy modelling. A lightweight physics-based energy model was integrated into a framework to capture kinematics data, such as aerodynamic drag and inertial dynamics in real time. This thesis focuses on evaluating multiple control strategies for energy efficiency under controlled scenarios. Three controllers were evaluated: Adaptive Cruise Control (ACC), Cooperative Adaptive Cruise Control(CACC), and proposed energy-aware controller. The energy-aware controller provided greater energy savings than the other two controllers with savings over 20% along with strongest string stability. This thesis provides insights into energy benefits that can be gained through energy-aware controllers working with information sharing in autonomous platooning, allowing for deeper insights relevant to Canadian transportation decarbonization.","abstract_has_math":false,"creators":["Khalid, Muhammad Zaeem"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Software Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Azim, Akramul"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-01","date_published":"2025-12-01","updated_at":"2026-07-24T05:35:38Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/2053","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Azim, Akramul"]},{"key":"dc:creator","label":"Author","values":["Khalid, Muhammad Zaeem"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-20T20:26:07Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Software Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/2053"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The increasing need for energy in most systems has resulted in the need for efficient energy usage, particularly in vehicle-based systems. This thesis investigates energy-efficient platooning of Connected and Autonomous Vehicles(CAVs) in simulation frameworks using energy modelling. A lightweight physics-based energy model was integrated into a framework to capture kinematics data, such as aerodynamic drag and inertial dynamics in real time. This thesis focuses on evaluating multiple control strategies for energy efficiency under controlled scenarios. Three controllers were evaluated: Adaptive Cruise Control (ACC), Cooperative Adaptive Cruise Control(CACC), and proposed energy-aware controller. The energy-aware controller provided greater energy savings than the other two controllers with savings over 20% along with strongest string stability. This thesis provides insights into energy benefits that can be gained through energy-aware controllers working with information sharing in autonomous platooning, allowing for deeper insights relevant to Canadian transportation decarbonization."]},{"key":"dc:title","label":"Title","values":["Optimized and energy-efficient cooperative platooning for autonomous vehicles"]}]}],"canonical_facts":{"dc:contributor.advisor":["Azim, Akramul"],"dc:creator":["Khalid, Muhammad Zaeem"],"dc:date.accessioned":["2026-01-20T20:26:07Z"],"dc:date.issued":["2025-12-01"],"dc:description.abstract":["The increasing need for energy in most systems has resulted in the need for efficient energy usage, particularly in vehicle-based systems. This thesis investigates energy-efficient platooning of Connected and Autonomous Vehicles(CAVs) in simulation frameworks using energy modelling. A lightweight physics-based energy model was integrated into a framework to capture kinematics data, such as aerodynamic drag and inertial dynamics in real time. This thesis focuses on evaluating multiple control strategies for energy efficiency under controlled scenarios. Three controllers were evaluated: Adaptive Cruise Control (ACC), Cooperative Adaptive Cruise Control(CACC), and proposed energy-aware controller. The energy-aware controller provided greater energy savings than the other two controllers with savings over 20% along with strongest string stability. This thesis provides insights into energy benefits that can be gained through energy-aware controllers working with information sharing in autonomous platooning, allowing for deeper insights relevant to Canadian transportation decarbonization."],"dc:identifier.uri":["https://hdl.handle.net/10155/2053"],"dc:language.iso":["en"],"dc:title":["Optimized and energy-efficient cooperative platooning for autonomous vehicles"],"dc:type":["Thesis"],"thesis:degree_discipline":["Software Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:38Z"}