Heuristic-based Speed Profile Generation for Multi-Train Simulator

Morais, Vítor; Rocha, Agostinho; Afonso, João L.; Martins, A.

doi:10.1109/is.2018.8710483

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…In Fig. 3 is presented an AC 1x25 kV electrification scheme, which is the system under study in [29], having the sub-urban trains with a nominal power consumption of 1.4 MW. However, this work targets any type of train, as long as it has capabilities for regenerative braking operation.…”

Section: Materials and Frameworkmentioning

confidence: 99%

Towards Smart Railways: A Charging Strategy for Railway Energy Storage Systems

Morais¹,

Afonso²,

Martins³

2021

EAI Endorsed Transactions on Energy Web

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The huge power requirements of future railways require the usage of energy-efficient strategies towards a more intelligent railway system. The usage of on-board energy storage systems enables better usage of the traction energy with a higher degree of freedom. In this article is proposed a top-level charging controller for the on-board and wayside railway energy storage systems. Its structure comprehends two processing levels: a real-time fuzzy logic controller for each energy storage system, and a genetic algorithm meta-heuristic, that remotely and automatically tune the fuzzy rules weight. As global results, the reduction of regenerated energy is 22.3% with the fuzzy logic controller. With the optimization strategy, this reduction can be further extended to 28.7%. The need for a smart railway framework is also discussed towards a realistic implementation of such charging strategy. Thus, with a high degree of flexibility, the efficiency of railway energy systems can be increased with the proposed framework.

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Section: Materials and Frameworkmentioning

confidence: 99%

Towards Smart Railways: A Charging Strategy for Railway Energy Storage Systems

Morais¹,

Afonso²,

Martins³

2021

EAI Endorsed Transactions on Energy Web

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“…One of the basic operational scenarios implemented by passenger trains is driving between two stopping points, e.g., stations [24]. The algorithm of such a scenario consists of the following steps: Practical examples of driving according to the above algorithm are those specific to suburban and metro traffic, where the distance between stops is small (1-2 km) The model of train movement for a similar scenario has been analyzed in [24][25][26].…”

Section: Train Driving Modelmentioning

confidence: 99%

“…This model shows the optimal train speed profile from an indicator point of view. Its individual stages are well characterized by the traction force (μf) and braking force (μb) [26]. The stages to be distinguished are: Figure 1 presents the optimal vehicle speed profile.…”

Section: Train Driving Modelmentioning

confidence: 99%

Testing the Smooth Driving of a Train Using a Neural Network

Koper

Kochan

2020

Sustainability

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This article deals with the extraction of a new original parameter to characterize a railway traffic driving smoothness indicator, and its investigation is based on data obtained from a neural train emulator. This indicator of driving smoothness is an example of the sustainable value of control command and signaling technology. The pro-social and pro-environmental aspects of smooth driving are indicated and the article proposes the introduction of a new indicator for assessing the quality of rail traffic, taking into account traffic on a micro scale—the driving smoothness of a single train (also called driving flow), derived from a parameter identified in the literature—and traffic smoothness (also called traffic flow), describing traffic quality on a macro scale. At the same time, the concept of a neural train emulator is presented, providing input data to determine the value of the proposed indicator for different train models and track systems in order to test the indicator’s properties. The concept proposes the structure of an artificial neural network, the technique of obtaining test data sets and the conditions of training the network as well. An emulator based on the neural network enables the simulation of train driving, taking into account its nonlinearity and data acquisition for indicator research.

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