{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1186"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1186","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Development of eco-driving control system for connected and automated hybrid electric vehicles","abstract":"Hybrid electric vehicles (HEVs) were designed as a potential solution to the ever-increasing global problems of the energy crisis and global warming through flexibly utilizing both fuel and electrical energy. Besides, the emerging technologies of connected and automated vehicles (CAVs) have provided huge possibilities to push the boundaries of HEVs even further and thus have been extensively studied. In this study, a bi-level MPC-based eco-driving strategy for CAHEVs is proposed and designed to improve fuel economy, reduce exhaust emissions while ensuring driving safety under the most common driving scenarios. First, the HEV powertrain is modelled, and the real-time data sources are in the intelligent transportation system (ITS) are introduced. Next, the multi-objective problem is formulated with three goals, namely, driving safety, fuel economy and emission reduction. The simulation is carried out on a map with realistic driving conditions. The results demonstrate the effectiveness and robustness of the proposed eco-driving strategy for CAHEVs.","abstract_html":"Hybrid electric vehicles (HEVs) were designed as a potential solution to the ever-increasing global problems of the energy crisis and global warming through flexibly utilizing both fuel and electrical energy. Besides, the emerging technologies of connected and automated vehicles (CAVs) have provided huge possibilities to push the boundaries of HEVs even further and thus have been extensively studied. In this study, a bi-level MPC-based eco-driving strategy for CAHEVs is proposed and designed to improve fuel economy, reduce exhaust emissions while ensuring driving safety under the most common driving scenarios. First, the HEV powertrain is modelled, and the real-time data sources are in the intelligent transportation system (ITS) are introduced. Next, the multi-objective problem is formulated with three goals, namely, driving safety, fuel economy and emission reduction. The simulation is carried out on a map with realistic driving conditions. The results demonstrate the effectiveness and robustness of the proposed eco-driving strategy for CAHEVs.","abstract_has_math":false,"creators":["Wang, Siyang"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Automotive Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Lin, Xianke"],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-04-01","date_published":"2020-04-01","updated_at":"2026-07-24T05:35:39Z","subjects":["Eco-driving","Energy management strategy (EMS)","Connected and automated hybrid vehicle (CAV)","Hybrid electric vehicle (HEV)","Intelligent transportation system (ITS)"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1186","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lin, Xianke"]},{"key":"dc:creator","label":"Author","values":["Wang, Siyang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-11-11T19:16:36Z","2022-03-29T16:46:16Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-11-11T19:16:36Z","2022-03-29T16:46:16Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Automotive 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":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Eco-driving","Energy management strategy (EMS)","Connected and automated hybrid vehicle (CAV)","Hybrid electric vehicle (HEV)","Intelligent transportation system (ITS)"]}]},{"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/10155/1186"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Hybrid electric vehicles (HEVs) were designed as a potential solution to the ever-increasing global problems of the energy crisis and global warming through flexibly utilizing both fuel and electrical energy. Besides, the emerging technologies of connected and automated vehicles (CAVs) have provided huge possibilities to push the boundaries of HEVs even further and thus have been extensively studied. In this study, a bi-level MPC-based eco-driving strategy for CAHEVs is proposed and designed to improve fuel economy, reduce exhaust emissions while ensuring driving safety under the most common driving scenarios. First, the HEV powertrain is modelled, and the real-time data sources are in the intelligent transportation system (ITS) are introduced. Next, the multi-objective problem is formulated with three goals, namely, driving safety, fuel economy and emission reduction. The simulation is carried out on a map with realistic driving conditions. The results demonstrate the effectiveness and robustness of the proposed eco-driving strategy for CAHEVs."]},{"key":"dc:title","label":"Title","values":["Development of eco-driving control system for connected and automated hybrid electric vehicles"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lin, Xianke"],"dc:creator":["Wang, Siyang"],"dc:date.accessioned":["2020-11-11T19:16:36Z","2022-03-29T16:46:16Z"],"dc:date.available":["2020-11-11T19:16:36Z","2022-03-29T16:46:16Z"],"dc:date.issued":["2020-04-01"],"dc:description.abstract":["Hybrid electric vehicles (HEVs) were designed as a potential solution to the ever-increasing global problems of the energy crisis and global warming through flexibly utilizing both fuel and electrical energy. Besides, the emerging technologies of connected and automated vehicles (CAVs) have provided huge possibilities to push the boundaries of HEVs even further and thus have been extensively studied. In this study, a bi-level MPC-based eco-driving strategy for CAHEVs is proposed and designed to improve fuel economy, reduce exhaust emissions while ensuring driving safety under the most common driving scenarios. First, the HEV powertrain is modelled, and the real-time data sources are in the intelligent transportation system (ITS) are introduced. Next, the multi-objective problem is formulated with three goals, namely, driving safety, fuel economy and emission reduction. The simulation is carried out on a map with realistic driving conditions. The results demonstrate the effectiveness and robustness of the proposed eco-driving strategy for CAHEVs."],"dc:identifier.uri":["https://hdl.handle.net/10155/1186"],"dc:language.iso":["en"],"dc:subject":["Eco-driving","Energy management strategy (EMS)","Connected and automated hybrid vehicle (CAV)","Hybrid electric vehicle (HEV)","Intelligent transportation system (ITS)"],"dc:title":["Development of eco-driving control system for connected and automated hybrid electric vehicles"],"dc:type":["Thesis"],"thesis:degree_discipline":["Automotive Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:39Z"}