Train speed profiles optimization using a genetic algorithm based on a random-forest model to estimate energy consumption

Ahmed Amrani (1) , Amira Ben Hamida (1) , Tao Liu (2) , Olivier Langlois (2)

1 IRT SystemX
2 ALSTOM - ALSTOM

The most important part of the train's energy is consumed by the traction system. The tractive energy depends mainly on the driving behaviour. Improving driving strategies has great potential to enhance the energy efficiency. This paper presents a speed profile optimization approach based on a genetic algorithm. The objective of the genetic algorithm is to find, for each interstation, the best speed profile which minimizes the energy consumption. The optimized profile takes into account both the physical and the operational constraints such as the maximum allowed travel time, the speed limitations per section and the maximum allowed acceleration and jerk. The fitness function is based on a Random Forest model which is built using on-board measurements. The aim of the model is to estimate accurately the energy consumption corresponding to each speed profile. The initial population of genetic algorithm is mainly composed of the best realistic speed profiles extracted from the collected data.

Mots clés: energy optimization; genetic algorithm; speed profile; machine learning; random forest

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]; Machine Learning [stat.ML]

Ahmed Amrani, Amira Ben Hamida, Tao Liu, Olivier Langlois. Train speed profiles optimization using a genetic algorithm based on a random-forest model to estimate energy consumption. Transport Research Arena (TRA) 2018, Apr 2018, vienne, Austria. ⟨hal-01767006⟩