Design of Energy-Efficient Train Speed Profiles Considering Fixed-Block Signalling System

2018 ◽  
Vol 138 (4) ◽  
pp. 282-290 ◽  
Author(s):  
Naoki Oba ◽  
Masafumi Miyatake
Keyword(s):  
Energy Efficient ◽  
Signalling System ◽  
Train Speed

Kinematic formulation of energy-efficient train speed profiles

2015 ◽  
Author(s):  
Fulvio Simonelli ◽  
Mariano Gallo ◽  
Vittorio Marzano
Keyword(s):  
Energy Efficient ◽  
Train Speed

scholarly journals Energy-Efficient Driving Strategies for Multi-Train by Optimization and Update Speed Profiles Considering Transmission Losses of Regenerative Energy

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3573 ◽  
Author(s):  
Chen ◽  
Xiao ◽  
Sun ◽  
Wang ◽  
Jin ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Efficient ◽  
Urban Rail Transit ◽  
Electrical Network ◽  
Optimization Strategy ◽  
Total Energy Consumption ◽  
Optimal Control Strategy ◽  
Train Speed ◽  
Regenerative Energy

This paper aims at minimizing the total energy consumption of multi-train in an urban rail transit (URT) system by optimizing and updating speed profiles considering regenerative braking power losses on the catenary. To make full use of regenerative energy and decrease traction energy consumption simultaneously, energy-efficient control strategies of multi-train and a corresponding solution method are proposed. The running process of multi-train is divided into several sections based on passenger stations. Speed profiles of each train in each section are collaboratively optimized by searching only one transition point from the optimized single-train speed profile, which can be worked out by searching the switching point of coasting mode, and the optimized multi-train speed profiles are updated based on departure orders of trains. Moreover, an electrical network model is established to analyze energy flows, and dynamic losses of recovered regenerative energy on the line can be calculated. Besides, an improved optimization strategy of multi-train, which contains seven motion phases, is presented for steep slope. Simulation results based on Guangzhou Metro Line 8 verify the effectiveness of the proposed methods. Total energy consumption of optimized multi-train can be decreased by 6.95% compared with multi-train adopted single-train optimal control strategy, and the energy-saving rate of 21.08% can be achieved compared with the measured data by drivers under same trip time. In addition, the influence of departure interval on total energy consumption is analyzed and the optimal departure interval can be obtained.

Energy-Efficient Train Speed Profile Generator Combining Partial Energy-Oriented Driving Approaches

2016 ◽  
Vol 196 (1) ◽  
pp. 42-51
Author(s):  
KAZUMASA KUMAZAWA ◽  
KEISUKE SATO ◽  
TOMOYUKI OGAWA
Keyword(s):  
Energy Efficient ◽  
Speed Profile ◽  
Partial Energy ◽  
Train Speed

Design of energy-efficient train speed profiles considering fixed-block signaling system

2018 ◽  
Vol 205 (1) ◽  
pp. 26-35
Author(s):  
Naoki Oba ◽  
Masafumi Miyatake
Keyword(s):  
Energy Efficient ◽  
Signaling System ◽  
Train Speed

Energy-Efficient Train Speed Profile Generator by Combining Partial Energy-Oriented Driving Approaches

2015 ◽  
Vol 135 (4) ◽  
pp. 368-375 ◽  
Author(s):  
Kazumasa Kumazawa ◽  
Keisuke Sato ◽  
Tomoyuki Ogawa
Keyword(s):  
Energy Efficient ◽  
Speed Profile ◽  
Partial Energy ◽  
Train Speed

Energy‐efficient approach combining train speed profile and timetable optimisations for metro operations

2020 ◽  
Vol 14 (14) ◽  
pp. 1967-1977
Author(s):  
Xin‐Chen Ran ◽  
Shao‐Kuan Chen ◽  
Ge‐Hui Liu ◽  
Yun Bai
Keyword(s):  
Energy Efficient ◽  
Speed Profile ◽  
Efficient Approach ◽  
Train Speed

Performance Evaluation of AFOD Protocol for Energy Efficient Routing

2011 ◽  
Author(s):  
B. Smitha Shekar ◽  
M. Sudhakar Pillai ◽  
G. Narendra Kumar
Keyword(s):  
Performance Evaluation ◽  
Energy Efficient ◽  
Energy Efficient Routing

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

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