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

M. Sira, A.P. Cucala, A. Fernández-Cardador, A. Fernández Rodríguez

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.


Palabras clave: uncertainty, Monte Carlo methods, energy consumption, railway engineering.

Conference on Precision Electromagnetic Measurements, Denver, Colorado (Estados Unidos de América). 24 agosto 2020

DOI: DOI icon 10.1109/CPEM49742.2020.9191703    

Fecha de publicación: agosto 2020.



Cita:
M. Sira, A.P. Cucala, A. Fernández-Cardador, A. Fernández Rodríguez, Sensitivities and uncertainties of eco-driving algorithm estimating train power consumption, Conference on Precision Electromagnetic Measurements - CPEM 2020, Denver, Estados Unidos de América, 24-28 Agosto 2020.


    Líneas de investigación:
  • Conducción económica de trenes y Ecodriving

IIT-20-140A

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