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

日益增长的环境问题和降低能源消耗的需求促使混合动力推进系统技术迅速发展。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、设计优化和驾驶员或任务自适应实时最优控制技术，以使混合动力推进技术充分发挥其性能、能效、减排和生命周期成本节约潜力。该研究将在三个密切相关的领域进行：a)开发动态、实时最优控制的知识和技术，这些控制是根据驾驶员或船舶的实际操作进行优化的，而不是目前固定的标准驾驶周期；B)为混合动力船舶优化设计与控制开发建立新的方法和建模工具平台；c)深入了解基于元模型的全局优化（MBGO）技术，并开发系统的算法/工具，用于下一代插电式混合动力汽车和船舶（PHEV/ phees）的设计和控制优化。基于行程的驾驶员自适应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获取的运行数据中识别车辆和船舶运行模式，并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。船舶混合动力推进系统建模研究将填补现有研究和工业实践的空白，为设计和控制优化提供一个具有适当复杂性和保真度的综合顶层系统模型。改进和验证减阶水动力船舶阻力和螺旋桨推力模型将成为建模研究的一部分。这些复杂的设计和控制优化问题形成了计算密集型的黑盒全局优化问题，需要更高效、鲁棒和高维的全局优化技术。对先进MBGO理论和算法的不断研究将满足这一需求。本研究将混合动力推进系统模型与先进的优化技术相结合，形成新的基于模型的设计与优化技术。该研究项目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，为大量具有前沿研究和实践经验的hqp提供宝贵的培训，并解决工业的迫切需求。

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.