Analysis of a Hydrogen Powered Train Performance using Inverse Simulation and Biologically Inspired Optimization Techniques

使用逆向仿真和仿生优化技术分析氢动力列车性能

• 批准号: 2907952
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2907952
• 关键词: Analysis; Hydrogen; Powered; Train; Performance

Future generations of transportation will be powered by sustainable sources of energy that are carried locally on the vehicles themselves which will eliminate the need for traditional combustion engines. This is particularly prevalent for rail vehicles where it is envisaged that the power system will be based on Hydrogen Fuel Cell technology. The vision for Scotland's passenger and freight railway network is that many lines will be electrified as detailed in the Rail Services Decarbonisation Action Plan. The aforementioned on-board power generation for trains will complement the overhead power infrastructure and provide the necessary power requirements for unelectrified routes, in particular routes around the West Highland Line and northern Scotland.The performance of rail vehicles depends on the power-train design for the particular vehicle and the energy management strategy. In addition to these key design elements, the dynamics of the vehicle and the relationship between the wheels and the rails need to be understood. Naturally these elements vary during different operations (e.g. approaching/leaving stations), different track configurations (e.g. inclines and declines, curves and points) and inclement weather conditions. Incorporating these elements within a simulation based on a mathematical model of the train can allow greater understanding of the vehicle's performance. In this study, mathematical models of train dynamics, refer to as forward models, will be used to analysis the power requirements for given track topologies. These models can be used within an Inverse Simulation framework to determine the performance and associated power requirements for the specified journey that the train will undertake. This analysis will enable the power-train and associated resources, e.g. quantity of hydrogen, to be specified based on the performance requirements of the train and track provided.Power-train modelling with regards to hydrogen fuel cells and other power components such as batteries and supercapacitors can form an optimisation problem. It is proposed that the Inverse Simulation will be used in conjunction with biologically inspired optimisation algorithms, such as the genetic algorithm and ant colony optimisation, to determine the number of fuel cell stacks and complementary power components required for a given locomotive, train and rail route.

未来几代交通工具将由当地车辆本身携带的可持续能源提供动力，这将消除对传统内燃机的需求。这在轨道车辆中尤其普遍，因为预计动力系统将基于氢燃料电池技术。苏格兰客运和货运铁路网的愿景是，许多线路将按照铁路服务脱碳行动计划的细节进行电气化。上述列车车载发电将补充架空电力基础设施，并为未电气化的路线提供必要的电力需求，特别是围绕西高地线和苏格兰北部的路线。轨道车辆的性能取决于特定车辆的动力总成设计和能源管理策略。除了这些关键的设计元素外，还需要了解车辆的动力学以及车轮和轨道之间的关系。当然，这些要素在不同的作业(如进出站)、不同的轨道配置(如坡度和坡度、曲线和点)以及恶劣天气条件下会有所不同。将这些元素纳入基于列车数学模型的仿真中，可以更好地了解车辆的性能。在这项研究中，列车动力学的数学模型，称为正向模型，将被用来分析给定轨道拓扑的功率需求。这些模型可以在反向模拟框架内使用，以确定列车将进行的指定旅程的性能和相关的功率要求。这种分析将使动力总成和相关资源，例如氢气量，能够根据所提供的列车和轨道的性能要求来指定。关于氢燃料电池和其他动力部件(如电池和超级电容器)的动力总成建模可能会形成优化问题。建议将反向模拟与生物启发的优化算法(如遗传算法和蚁群优化)结合使用，以确定给定机车、火车和铁路线所需的燃料电池组和互补动力部件的数量。