Modeling, Optimization and Real-time Optimal Control of Hybrid Electric Vehicles and Marine Vessels

混合动力电动汽车和船舶的建模、优化和实时优化控制

• 批准号: RGPIN-2017-06219
• 负责人: Dong, Zuomin
• 金额: $ 2.26万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712097
• 关键词: Modeling; Optimization; Real; time; Optimal

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.

日益增长的环境问题和降低能耗的需求已经引起了混合电力推进系统技术的快速发展。拟议的研究计划旨在解决阻碍该技术进一步发展及其在重型车辆和船舶上更广泛的运输应用的几个基本问题。该研究将引入先进的建模、设计优化以及驾驶员或使命自适应实时优化控制技术，以使混合动力推进技术充分发挥其性能、能效、减排和寿命周期成本节约的潜力。 研究将在三个密切相关的领域进行：a）开发基于驾驶员或船舶的实际操作而不是目前固定的标准驾驶循环进行优化的动态、实时优化控制的知识和技术：B）建立用于混合动力船舶优化设计和控制开发的新方法和建模工具平台;以及c）形成对基于元模型的全局优化（MBGO）技术的透彻理解和系统算法/工具开发，用于下一代插电式混合动力电动车辆和船舶（PHEV/PHES）的设计和控制优化。基于行程的、驾驶员自适应的智能最优功率控制和能量管理技术及其自学习能力将基于从系统采集的操作数据中识别车辆和船舶操作模式，以及使用考虑电池性能退化的混合动力推进系统模型生成离线最优控制计划。混合电力推进系统建模研究将弥补目前研究和工业实践中存在的空白，为设计和控制优化提供具有适当复杂度和保真度的集成顶层系统模型。 模型研究的一部分是对船舶水动力阻力和螺旋桨推力降阶模型的改进和验证。这些复杂的设计和控制优化问题形成了计算密集型的黑箱全局优化问题，需要更高效、鲁棒和高维的全局优化技术。 对先进的MBGO理论和算法的不断研究将满足这一需求。 本研究将联合收割机混合动力系统模型与先进的优化技术相结合，形成基于模型的设计与优化技术。该研究计划将提高我们利用混合动力推进和优化技术的理解和能力，开辟新的研究领域，为大量具有尖端研究和实践经验的HQP提供宝贵的培训，并解决行业的迫切需求。