Reference number : GC-2017-Q1-03126, amendment number : , agreement type : G, recipient legal name : Dong, Zuomin (University of Victoria), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Growing environmental concerns and the need to reduce energy consumption have given rise to rapid advances in hybrid electric propulsion system technology. The proposed research program is aimed at addressing several fundamental issues that are hindering the further advance of the technology and its broader transportation applications to heavy-duty vehicles and marine vessels. The research will introduce advanced modeling, design optimization, and driver or mission adaptive real-time optimal control techniques to allow the hybrid propulsion technology to reach its full performance, energy efficiency, emissions reduction and life-cycle cost saving potentials. The research will be carried out in three closely related areas: a) developing the knowledge and techniques for dynamic, real-time optimal controls that are optimized based on the actual operations of a driver or a ship, not on the fixed standard driving cycles as present; b) establishing the new methodology and modeling tool platform for the optimal design and control developments of hybrid electric marine vessels; and c) forming a thorough understanding and systematic algorithm/tool development of Metamodel Based Global Optimization (MBGO) techniques for the design and control optimizations of the next generation Plug-in Hybrid Electric Vehicles and Ships (PHEV/PHES). The trip-based, driver adaptive intelligent optimal power control and energy management techniques, and its self-learning capability will be based on vehicle and ship operation pattern identification from systematically acquired operation data, and off-line optimal control plan generation using the hybrid propulsion system model with battery performance degradation consideration. The hybrid electric marine propulsion system modeling research will fulfill the void existed in present research and industrial practice to produce an integrated top-level system model with appropriate complexity and fidelity for design and control optimization. Improvement and validation of reduced-order hydrodynamic ship drag and propeller thrust models will form part of the modeling research. These complex design and control optimization problems form computationally intensive black-box global optimization problems, and calls for more efficient, robust and high dimensional global optimization techniques. The continued study on advanced MBGO theory and algorithms will meet this need. The proposed research will combine the hybrid propulsion system model and advanced optimization to form the new Model Based Design and Optimization technology. The research program will improve our understanding and ability to utilize hybrid propulsion and optimization technologies, open new research areas, provide invaluable training for a large number of HQPs with cutting edge research and hands-on experiences, and address the urgent needs from industry., program purpose french : Growing environmental concerns and the need to reduce energy consumption have given rise to rapid advances in hybrid electric propulsion system technology. The proposed research program is aimed at addressing several fundamental issues that are hindering the further advance of the technology and its broader transportation applications to heavy-duty vehicles and marine vessels. The research will introduce advanced modeling, design optimization, and driver or mission adaptive real-time optimal control techniques to allow the hybrid propulsion technology to reach its full performance, energy efficiency, emissions reduction and life-cycle cost saving potentials. The research will be carried out in three closely related areas: a) developing the knowledge and techniques for dynamic, real-time optimal controls that are optimized based on the actual operations of a driver or a ship, not on the fixed standard driving cycles as present; b) establishing the new methodology and modeling tool platform for the optimal design and control developments of hybrid electric marine vessels; and c) forming a thorough understanding and systematic algorithm/tool development of Metamodel Based Global Optimization (MBGO) techniques for the design and control optimizations of the next generation Plug-in Hybrid Electric Vehicles and Ships (PHEV/PHES). The trip-based, driver adaptive intelligent optimal power control and energy management techniques, and its self-learning capability will be based on vehicle and ship operation pattern identification from systematically acquired operation data, and off-line optimal control plan generation using the hybrid propulsion system model with battery performance degradation consideration. The hybrid electric marine propulsion system modeling research will fulfill the void existed in present research and industrial practice to produce an integrated top-level system model with appropriate complexity and fidelity for design and control optimization. Improvement and validation of reduced-order hydrodynamic ship drag and propeller thrust models will form part of the modeling research. These complex design and control optimization problems form computationally intensive black-box global optimization problems, and calls for more efficient, robust and high dimensional global optimization techniques. The continued study on advanced MBGO theory and algorithms will meet this need. The proposed research will combine the hybrid propulsion system model and advanced optimization to form the new Model Based Design and Optimization technology. The research program will improve our understanding and ability to utilize hybrid propulsion and optimization technologies, open new research areas, provide invaluable training for a large number of HQPs with cutting edge research and hands-on experiences, and address the urgent needs from industry., agreement title english : Modeling, Optimization and Real-time Optimal Control of Hybrid Electric Vehicles and Marine Vessels, agreement title french : Modeling, Optimization and Real-time Optimal Control of Hybrid Electric Vehicles and Marine Vessels, agreement number : RGPIN, agreement value : 155000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada