Abstract

This paper presents a speed profile generation approach for longitudinal control of automated vehicles, based on quintic Bézier curves. The described method aims to increase comfort level of passengers based on the ISO2631-1 specification, while taking into account vehicle dynamics and traffic rules to keep high safety levels. The algorithm has been tested in an in-house tool for high accuracy vehicle dynamics simulations, called Dynacar. The considered scenario is a closed circuit inside Tecnalia facilities. The resulting profile has better properties (for example, rate of change) than a raw input based on traffic speed limits. When used as reference for the speed controller, it improves both comfort and safety. This work is partly supported by the H2020 project UnCoVerCPS with grant number 643921, and the H2020 Project STEVE with proposal identification number 769944.

Abstract

The aim of the research is to identify factors influencing real utilisation of the maximum line speed. On"br" infrastructure side main factor seems to be differentiation of the maximum speed along the line, which is taken"br" into account with the harmonic weighted mean. As far as rolling stock is concerned main factors are type of train"br" formation (loco-hauled, EMU, DMU), power-to-weight ratio and percentage of powered axles. The extensive"br" database covering the infrastructure data and rolling stock data for intercity train services in various European"br" countries has been prepared. The best utilisation of maximum line speed is in the case of long sections (300 km or"br" more) passed without intermediate stops, at which the influence of acceleration and braking is relatively minor."br" The utilisation of maximum speed is negatively influenced by significant differentiation of the speed profile"br" (frequent and large changes of speed along the line). The most effective utilisation of line capabilities is in the case"br" of Electric Motor Units (EMUs) with distributed power and high power-to-weight ratio.

Abstract

International audience; 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. Document type: Conference object