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Application of Genetic Algorithms for Driverless Subway Train Energy Optimization


Affiliations
1 Politecnico di Milano, Department of Energy, Via La Masa 34, 20156 Milano, Italy
 

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.
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  • Application of Genetic Algorithms for Driverless Subway Train Energy Optimization

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Authors

Morris Brenna
Politecnico di Milano, Department of Energy, Via La Masa 34, 20156 Milano, Italy
Federica Foiadelli
Politecnico di Milano, Department of Energy, Via La Masa 34, 20156 Milano, Italy
Michela Longo
Politecnico di Milano, Department of Energy, Via La Masa 34, 20156 Milano, Italy

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.