\"This dissertation offers a description of the development of a fuel cell plug-in hybrid electric vehicle focusing on the propulsion architecture selection, propulsion system control, and high-level energy management. Two energy management techniques have been developed and implemented for real-time control of the vehicle. The first method is a heuristic method that relies on a short-term moving average of the vehicle power requirements. The second method utilizes an affine function of the short-term and long-term moving average vehicle power requirements. The development process of these methods has required the creation of a vehicle simulator capable of estimating the effect of changes to the energy management control techniques on the overall vehicle energy efficiency. Furthermore, the simulator has allowed for the refinement of the energy management methods and for the stability of the method to be analyzed prior to on-road testing. This simulator has been verified through on-road testing of a constructed prototype vehicle under both highway and city driving schedules for each energy management method. The results of the finalized vehicle control strategies are compared with the simulator predictions and an assessment of the effectiveness of both strategies is discussed. The methods have been evaluated for energy consumption in the form of both hydrogen fuel and stored electricity from grid charging\"--Abstract, page iii.
","abstract_html":"<p>"This dissertation offers a description of the development of a fuel cell plug-in hybrid electric vehicle focusing on the propulsion architecture selection, propulsion system control, and high-level energy management. Two energy management techniques have been developed and implemented for real-time control of the vehicle. The first method is a heuristic method that relies on a short-term moving average of the vehicle power requirements. The second method utilizes an affine function of the short-term and long-term moving average vehicle power requirements. The development process of these methods has required the creation of a vehicle simulator capable of estimating the effect of changes to the energy management control techniques on the overall vehicle energy efficiency. Furthermore, the simulator has allowed for the refinement of the energy management methods and for the stability of the method to be analyzed prior to on-road testing. This simulator has been verified through on-road testing of a constructed prototype vehicle under both highway and city driving schedules for each energy management method. The results of the finalized vehicle control strategies are compared with the simulator predictions and an assessment of the effectiveness of both strategies is discussed. The methods have been evaluated for energy consumption in the form of both hydrogen fuel and stored electricity from grid charging"--Abstract, page iii.</p>","abstract_has_math":false,"creators":["Meintz, Andrew"],"institution":"Missouri University of Science and Technology","degree_name":"Ph. D. in Electrical Engineering","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-02-10T08:00:00Z","date_published":"2016-02-10T08:00:00Z","updated_at":"2026-07-24T03:18:50Z","subjects":["Supervisory control","Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/19","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Meintz, Andrew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-02-10T08:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D. in Electrical Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Missouri University of Science and Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Supervisory control","Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsmine.mst.edu/doctoral_dissertations/19"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"This dissertation offers a description of the development of a fuel cell plug-in hybrid electric vehicle focusing on the propulsion architecture selection, propulsion system control, and high-level energy management. Two energy management techniques have been developed and implemented for real-time control of the vehicle. The first method is a heuristic method that relies on a short-term moving average of the vehicle power requirements. The second method utilizes an affine function of the short-term and long-term moving average vehicle power requirements. The development process of these methods has required the creation of a vehicle simulator capable of estimating the effect of changes to the energy management control techniques on the overall vehicle energy efficiency. Furthermore, the simulator has allowed for the refinement of the energy management methods and for the stability of the method to be analyzed prior to on-road testing. This simulator has been verified through on-road testing of a constructed prototype vehicle under both highway and city driving schedules for each energy management method. The results of the finalized vehicle control strategies are compared with the simulator predictions and an assessment of the effectiveness of both strategies is discussed. The methods have been evaluated for energy consumption in the form of both hydrogen fuel and stored electricity from grid charging\"--Abstract, page iii.
"]},{"key":"dc:title","label":"Title","values":["Development of a fuel cell plug-in hybrid electric vehicle and vehicle simulator for energy management assessment"]}]}],"canonical_facts":{"dc:creator":["Meintz, Andrew"],"dc:date.available":["2016-02-10T08:00:00Z"],"dc:description.abstract":["\"This dissertation offers a description of the development of a fuel cell plug-in hybrid electric vehicle focusing on the propulsion architecture selection, propulsion system control, and high-level energy management. Two energy management techniques have been developed and implemented for real-time control of the vehicle. The first method is a heuristic method that relies on a short-term moving average of the vehicle power requirements. The second method utilizes an affine function of the short-term and long-term moving average vehicle power requirements. The development process of these methods has required the creation of a vehicle simulator capable of estimating the effect of changes to the energy management control techniques on the overall vehicle energy efficiency. Furthermore, the simulator has allowed for the refinement of the energy management methods and for the stability of the method to be analyzed prior to on-road testing. This simulator has been verified through on-road testing of a constructed prototype vehicle under both highway and city driving schedules for each energy management method. The results of the finalized vehicle control strategies are compared with the simulator predictions and an assessment of the effectiveness of both strategies is discussed. The methods have been evaluated for energy consumption in the form of both hydrogen fuel and stored electricity from grid charging\"--Abstract, page iii.
"],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/19"],"dc:subject":["Supervisory control","Electrical and Computer Engineering"],"dc:title":["Development of a fuel cell plug-in hybrid electric vehicle and vehicle simulator for energy management assessment"],"dc:type":["Dissertation - Open Access"],"thesis:degree_name":["Ph. D. in Electrical Engineering"],"thesis:institution_name":["Missouri University of Science and Technology"]},"updated_at":"2026-07-24T03:18:50Z"}