Application of Genetic Algorithms for Driverless Subway Train Energy Optimization

Abstract

After an introduction on the basic aspects of electric railway transports, focusing mainly on driverless subways and their related automation systems (ATC, ATP, and ATO), a technique for energy optimization of the train movement through their control using genetic algorithms will be presented. Genetic algorithms are a heuristic search and iterative stochastic method used in computing to find exact or approximate solutions to optimization problems. This optimization process has been calculated and tested on a real subway line in Milan through the implementation of a dedicated Matlab code. The so-defined algorithm provides the optimization of the trains movement through a coast control table created by the use of a genetic algorithm that minimizes the energy consumption and the train scheduled time. The obtained results suggest that the method is promising in minimizing the energy consumption of the electric trains.

Raylı Araçlarda Enerji Verimliliği Çalışması

“…where m is the mass of the rail vehicle, the numbers 0.06 and 0.15 refer to the coefficient related to the rotating mass and passenger mass respectively [20]. Equation 1 can be re-arranged as follows due to many forces acting on the rail vehicle during the motion of the rail vehicle:…”

“…where F m is the traction or braking force of the rail vehicle depending on the general characteristics of the engine. F rr is the rolling resistance, that is, the sum of the mechanical forces which act to prevent forward movement of a rail [19][20][21]. This force can be calculated as follows and corresponds to a different value for each speed of the rail vehicle.…”

“…where v is the speed of the rail vehicle (m/sec), α rr , b and c correspond to coefficients related to the mass, mechanical and air forces respectively [22]. When the rail vehicle is moving up or down, the gradient force (F rs ) generated parallel to the track can be calculated as follows (19)(20):…”

Survey on Driverless Train Operation for Urban Rail Transit Systems

“…In [36], an energy-efficient optimization approach is presented based on genetic algorithms, which reduces the energy consumption to 32.89% with a small increase (around 0.51%) in the travel time.…”

Impact of Symmetric Vertical Sinusoid Alignments on Infrastructure Construction Costs: Optimizing Energy Consumption in Metropolitan Railway Lines Using Artificial Neural Networks

“…The most common strategy is to focus on driving schemes because these allow higher rates of efficiency [6][7][8]. Previous studies have tried to reduce energy consumption focusing on manually driven trains [9,10] and on trains equipped with automatic train operation as well [11].…”