Local and global sensitivity analysis for railway upgrading between hydrogen fuel cell and electrification

In the field of rail transit, the UK Department of Transport stated that it will realize a comprehensive transformation of UK railways by 2050, abandoning traditional diesel trains and upgrading them to new environmentally friendly trains. The current mainstream upgrade methods are electrification and hydrogen fuel cells. Comprehensive upgrades are costly, and choosing the optimal upgrade method for trams and mainline railways is critical. Without a sensitivity analysis, it is difficult for us to determine the influence relationship between each parameter and cost, resulting in a waste of cost when choosing a line reconstruction method. In addition, by analyzing the sensitivity of different parameters to the cost, the primary optimization direction can be determined to reduce the cost. Global higher-order sensitivity analysis enables quantification of parameter interactions, showing non-additive effects between parameters. This paper selects the main parameters that affect the retrofit cost and analyzes the retrofit cost of the two upgrade methods in the case of trams and mainline railways through local and global sensitivity analysis methods. The results of the analysis show that, given the current UK rail system, it is more economical to choose electric trams and hydrogen mainline trains. For trams, the speed at which the train travels has the greatest impact on the final cost. Through the sensitivity analysis, this paper provides an effective data reference for the current railway upgrading and reconstruction plan and provides a theoretical basis for the next step of train parameter optimization. ● Develop cost models combining hybrid vehicle and infrastructure parameters to evaluate financial efficiency of upgrades. ● Perform local sensitivity analysis using the difference method on 12 key parameters affecting retrofit costs. ● Conduct global sensitivity analysis via Sobol index on 4 high-sensitivity parameters, showing cross-influences on upgrades.