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

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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.