OptiDrive: a practical approach for the calculation of energy-optimised operating speed profiles

Lechelle, S.A.; Mouneimne, Z.S.

doi:10.1049/ic.2010.0029

Cited by 18 publications

(12 citation statements)

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“…where is the mass of the vehicle. The equivalent mass of all rotating masses shall be set equal to 0.06 times the mass of the vehicle and it is estimated that the average mass of passengers transported is worth 0.15 times the mass of the vehicle, as suggested in [36]. It considers the vehicle or combination of vehicles as a concentrated mass .…”

Section: Description Of the Simulatormentioning

confidence: 99%

Application of Genetic Algorithms for Driverless Subway Train Energy Optimization

Brenna

Foiadelli

Longo

2016

International Journal of Vehicular Technology

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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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Section: Description Of the Simulatormentioning

confidence: 99%

Application of Genetic Algorithms for Driverless Subway Train Energy Optimization

Brenna

Foiadelli

Longo

2016

International Journal of Vehicular Technology

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“…The field test results showed that, by implementing the optimal speed profile, the tractive energy consumption can be significantly reduced. Meta-heuristic is also applied to solve the optimisation problem, such as genetic algorithm [10] and ant colony optimisation [11]. According to the short distance between stations and simple line condition of the metro transit system, the coasting point searching algorithm is applied to find the energy-efficient driving strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Vandanjon et al [16] proposed a method capable of producing a set of running times with associated tractive energy consumption that can be used to guide the running time distribution. However, these studies [3][4][5][6][7][8][9][10][11][12][13][14][15][16] ignore the utilisation of regenerative braking energy (RBE), which takes 33% of the total tractive energy consumption [17].…”

Section: Introductionmentioning

confidence: 99%

Two‐step method to reduce metro transit energy consumption by optimising speed profile and timetable

Jin

Sun

Wang

et al. 2020

IET Intelligent Transport Systems

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“…Several works proposed control algorithms based on continuous or automatic commands [27]- [29]. However, the solutions provided by these algorithms are not suitable for manual driving.…”

Section: Introductionmentioning

confidence: 99%

Energy efficiency in high speed railway traffic operation: a real-time ecodriving algorithm

Fernández-Rodríguez

Fernández-Cardador

Cucala

2015

2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

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The application of ecodriving strategies leads to important energy savings in high speed railway systems. Ecodriving techniques are usually offline implemented in the design of commercial services. However, efficient driving can also be online recalculated to regulate the deviations from the schedule of the train. In this paper, a dynamic multi-objective model is proposed to calculate a set of efficient drivings that are updated during the journey. Using this model it is possible to react when delays arise replacing the current driving by a faster one. The new speed profile will be energy efficient under the new conditions. The model is solved using the extension of NSGA-II for dynamic optimization problems (DNSGA-II). This algorithm is combined with a detailed simulator of the train motion. The accuracy of the simulator ensures the fulfillment of the quality restrictions in commercial services. The proposed method has been applied to a case study using real data from a Spanish high speed line. The performance of DNSGA-II solving the dynamic model has been compared with NSGA-II. Furthermore, energy savings provided by the regulation algorithm have been evaluated comparing it with the typical driving style when a delay is detected.

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OptiDrive: a practical approach for the calculation of energy-optimised operating speed profiles

Cited by 18 publications

References 0 publications

Application of Genetic Algorithms for Driverless Subway Train Energy Optimization

Application of Genetic Algorithms for Driverless Subway Train Energy Optimization

Two‐step method to reduce metro transit energy consumption by optimising speed profile and timetable

Energy efficiency in high speed railway traffic operation: a real-time ecodriving algorithm

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