Sensitivities and uncertainties of eco-driving algorithm estimating train power consumption

Abstract

This paper describes a study of uncertainty propagation through the Train Simulator Algorithm (TSA). The algorithm is used to estimate train driving time, consumed and regenerated energy. These output quantities are important to optimize the driving profile of the train and minimize energy spending. The uncertainty propagation was calculated using the Monte Carlo method. The sensitivity of output uncertainties on the input uncertainties was evaluated for two different train tracks in Spain, Madrid Metro, and in Italy, Bolonia-Ozzano. Results will be used to improve eco-driving profiles.This paper describes a study of uncertainty propagation through the Train Simulator Algorithm (TSA). The algorithm is used to estimate train driving time, consumed and regenerated energy. These output quantities are important to optimize the driving profile of the train and minimize energy spending. The uncertainty propagation was calculated using the Monte Carlo method. The sensitivity of output uncertainties on the input uncertainties was evaluated for two different train tracks in Spain, Madrid Metro, and in Italy, Bolonia-Ozzano. Results will be used to improve eco-driving profiles.