scholarly journals An Eco-Driving Algorithm for Interoperable Automatic Train Operation

2020 ◽  
Vol 10 (21) ◽  
pp. 7705
Author(s):  
Adrián Fernández-Rodríguez ◽  
Asunción P. Cucala ◽  
Antonio Fernández-Cardador
Keyword(s):  
Energy Consumption ◽  
Differential Evolution ◽  
Minimum Energy ◽  
Differential Evolution Algorithm ◽  
Multiple Time ◽  
Intermediate Target ◽  
Target Time ◽  
Traffic Regulation ◽  
Train Operation ◽  
Automatic Train Operation

The new Automatic Train Operation (ATO) system over the standard European Rail Traffic Management System (ERTMS) will specify the requirements that an automatic train driving system must fulfil in order to be interoperable. The driving is defined by target times located along the journey that are received from the trackside system. Then, the on-board equipment drives the train with the objective of meeting all of the target times. The use of eco-driving methods to calculate the train driving is necessary, as one of the main goals of modern train driving systems is to increase the energy efficiency. This paper presents a simulation-based optimisation algorithm to solve the eco-driving problem constrained by multiple target times. This problem aims to minimize the energy consumption subject to a commercial running time, as the classical eco-driving problem, and also to meet intermediate target times during the journey between stations to enable automatic traffic regulation, especially at junctions. The algorithm proposed combines a Differential Evolution procedure to generate possible solutions with a detailed train simulation model to evaluate them. The use of this algorithm makes possible to find accurate speed profiles that meet the requirements of multiple time objectives. The proposed Differential Evolution algorithm is capable of finding the feasible speed profile with the minimum energy consumption, obtaining a 7.7% of energy variation in the case of a journey with one intermediate target time and 3.1% in the case of two intermediate targets.

scholarly journals Research on the Utilization of Metro Regenerative Braking Energy Based on an Improved Differential Evolution Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Di Liu ◽  
Song-Qing Zhu ◽  
Yun-Rui Bi ◽  
Kun Liu ◽  
You-Xiong Xu
Keyword(s):  
Energy Consumption ◽  
Integer Programming ◽  
Differential Evolution ◽  
Total Energy ◽  
Differential Evolution Algorithm ◽  
Programming Models ◽  
Regenerative Braking ◽  
Total Energy Consumption ◽  
Train Operation ◽  
Improved Differential Evolution Algorithm

Urban metro trains have the characteristics of short running distance between stations and frequent starting and braking. A large amount of regenerative braking energy is generated during the braking process. The effective utilization of the regenerative braking energy can substantially reduce the total energy consumption of train operation. In this paper, we establish two integer programming models of train operation that maximize the overlap time between train traction and braking in peak hours and nonpeak hours. On this basis, an improved differential evolution (IDE) algorithm is developed for solving the two integer programming models. The results demonstrate that the overlap time increases by 51.44% after optimization using the IDE algorithm when the headway is set to 154 s in peak hours. The overlap time is further increased by 14.87% by optimizing the headway. In nonpeak hours, the overlap time of traction and braking of the trains in opposite directions at the same station is increased by optimizing the bidirectional departure interval, thereby reducing the total energy consumption of the system.

scholarly journals Differential Evolution Algorithm with Diversified Vicinity Operator for Optimal Routing and Clustering of Energy Efficient Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Subramaniam Sumithra ◽  
T. Aruldoss Albert Victoire
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Life Time ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Routing Problem

Due to large dimension of clusters and increasing size of sensor nodes, finding the optimal route and cluster for large wireless sensor networks (WSN) seems to be highly complex and cumbersome. This paper proposes a new method to determine a reasonably better solution of the clustering and routing problem with the highest concern of efficient energy consumption of the sensor nodes for extending network life time. The proposed method is based on the Differential Evolution (DE) algorithm with an improvised search operator called Diversified Vicinity Procedure (DVP), which models a trade-off between energy consumption of the cluster heads and delay in forwarding the data packets. The obtained route using the proposed method from all the gateways to the base station is comparatively lesser in overall distance with less number of data forwards. Extensive numerical experiments demonstrate the superiority of the proposed method in managing energy consumption of the WSN and the results are compared with the other algorithms reported in the literature.

scholarly journals Coking energy consumption radial basis function prediction model improved by differential evolution algorithm

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1122-1130 ◽  
Author(s):  
Wenhua Tao ◽  
Jiao Chen ◽  
Yunjin Gui ◽  
Pingping Kong
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Differential Evolution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Differential Evolution Algorithm ◽  
Function Prediction ◽  
Radial Basis ◽  
Evolution Algorithm

This paper presents a radial basis function prediction model improved by differential evolution algorithm for coking energy consumption process, which is very difficult to get online and real time because of the complex process. In the energy consumption prediction model, target flue temperature, flue suction, water content, volatile coal and coking time are considered as input variables, and coking energy consumption as output variables. To overcome the shortcomings of radial basis function network, such as poor learning ability and slow convergence speed, the energy consumption prediction model optimized by differential evolution algorithm is improved. Using the strong global search ability of differential evolution algorithm, the center value, width and output weight of the basis function in radial basis function network is obtained by differential evolution algorithm. Then the optimal values are taken as the center value, width and output weight of the of radial basis function neural network. The results show that the improved radial basis function prediction has higher accuracy, stability and training speed of the network. The radial basis function prediction model has great significance in reducing coking energy consumption, saving enterprise costs, increasing coke production and improving enterprise economic benefits.

Designing Automatic Train Regulation With Energy Saving by Using Dual Heuristic Programming

2011 ◽  
Author(s):  
Jih-Wen Sheu ◽  
Wei-Song Lin
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Large Scale ◽  
Cost Effective ◽  
Traffic Regulation ◽  
Time Control ◽  
Train Operation ◽  
Railway System ◽  
Scale Optimization ◽  
Environment Sustainability

Issues regarding environment sustainability and energy saving have been receiving concerns in worldwide railway society though railway system have been recognized as a transport mode of less environmental impact. Energy saving via train operation and regulation would be a cost-effective way and becomes a requirement while performing train operation and regulation. Automatic Train Regulation (ATR) plays an important role of maintaining the service quality of metro. However, designing ATR is a large scale optimization problem with high nonlinearity, heavy constraints, and stochastic characteristics. Considering issues regarding energy saving in the ATR design further complicates the problem. A metro traffic model which accounts for energy consumption is investigated in this paper. Thereby, Dual Heuristic dynamic Programming (DHP) technique is employed to design an optimal ATR with energy saving for metro line. Simulation tests of the ATR design were carried out with field data. Results show that better traffic regulation with less energy consumption is attainable through coasting and dwell time control.

A flexible and robust train operation model based on expert knowledge and online adjustment

2017 ◽  
Vol 15 (03) ◽  
pp. 1750023 ◽  
Author(s):  
Chun-Yang Zhang ◽  
Dewang Chen ◽  
Jiateng Yin ◽  
Long Chen
Keyword(s):  
Energy Consumption ◽  
Expert Knowledge ◽  
Operation Mode ◽  
Velocity Curve ◽  
Target Velocity ◽  
Optimization Approach ◽  
Operation Model ◽  
Train Control ◽  
Train Operation ◽  
Automatic Train Operation

Most existing automatic train operation (ATO) models are based on different train control algorithms and aim to closely track the target velocity curve optimized offline. This kind of model easily leads to some problems, such as frequent changes of the control outputs, inflexibility of real-time adjustments, reduced riding comfort and increased energy consumption. A new data-driven train operation (DTO) model is proposed in this paper to conduct the train control by employing expert knowledge learned from experienced drivers, online optimization approach based on gradient descent, and a heuristic parking method. Rather than directly to model the target velocity curve, the DTO model alternatively uses the online and offline operation data to infer the basic control output according to the domain expert knowledge. The online adjustment is performed over the basic output to achieve stability. The proposed train operation model is evaluated in a simulation platform using the field data collected in YiZhuang Line of Beijing Subway. Compared with the curve tracking approaches, the proposed DTO model achieves significant improvements in energy consumption and riding comfort. Furthermore, the DTO model has more advantages including the flexibility of the timetable adjustments and the less operation mode conversions, that are beneficial to the service life of train operation systems. The DTO model also shows velocity trajectories and operation mode conversions similar to the one of experienced drivers, while achieving less energy consumption and smaller parking error. The robustness of the proposed algorithm is verified through numerical simulations with different system parameters, complicated velocity restrictions, diverse running times and steep gradients.

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