Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology

Huang, Youneng; Yang, Chen; Gong, Shengkai

doi:10.3390/en9080626

Cited by 16 publications

(8 citation statements)

References 28 publications

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“…The fixed neutral section distribution in sub-cases 1 and 2 (EETC, EETC + EETT) is the practical neutral sections distribution in real, not set artificially. Incorporating NSLP into sub-case 3 is designed to FIGURE 11 The speed trajectory for both up and down trains under scheduled trip time 840 s improve regenerative braking utilization by adjusting the fixed neutral section distribution. Table 4 illustrates the final results of the three sub-cases by DLGA including section trip time, headway time, traction EC, maximum section CR speed, regenerative braking energy, reused regenerative braking, practical EC and so forth.…”

Section: Case 2: Joint Optimization Of Speed Trajectories Timetable A...mentioning

confidence: 99%

“…Then a determined timetable is established for each train. Based on Table 2, for each train in each section we can formulate a multi-phase model for single-train movement with Equation (11). Formulate the final objective function (17) with consideration of additional regenerative braking constraints ( 13)-( 16) and headway distance constraint (12) for successive trains.…”

Section: Integrated Problem Formulationmentioning

confidence: 99%

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Energy‐optimal study integrated speed trajectories, timetable and the layout of neutral sections for high‐speed railway

Sheng

Shangguan

Cai

et al. 2022

IET Intelligent Trans Sys

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Speed trajectories, timetable and the layout of neutral sections are the key elements that affect train energy consumption (EC). This paper combines the microscopic energy‐efficient train control (EETC), macroscopic energy‐efficient train timetabling (EETT) and neutral sections location planning (NSLP), to achieve minimum EC in bi‐directional high‐speed railway. An integrated model is first established based on neutral sections distribution model, timetable model and a multi‐phase model. Then, a double loop genetic algorithm (DLGA) is designed for matching the proposed model. The case studies are conducted by the proposed model and algorithm on the Wuhan‐Yueyang rail corridor. A simple case for single‐train movement in one section is primarily executed with variable neutral section location, to investigate the impact of neutral section location for train operation. The results indicate that neutral section locating at the coasting (CO) and cruising (CR) zones is the prior choice for energy conservation. Then, three integrated problems (EETC, EETC + EETT and EETC + EETT + NSLP) are carried out to obtain the energy‐optimal speed trajectories, timetable and the layout of neutral sections, showing that EETC + EETT + NSLP increases the regenerative energy recycling from 14.2% to 49.1% and achieves 9.2% energy reduction compared with EETC.

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Section: Case 2: Joint Optimization Of Speed Trajectories Timetable A...mentioning

confidence: 99%

Section: Integrated Problem Formulationmentioning

confidence: 99%

Energy‐optimal study integrated speed trajectories, timetable and the layout of neutral sections for high‐speed railway

Sheng

Shangguan

Cai

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Another example that uses ACO algorithm for speed profile optimization is presented in [15]. The generated speed profiles use a cycle of acceleration, cruising, coasting and braking.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recently, some research in the field of meta-heuristics, using nature-inspired structures and solution-searching mechanisms, has emerged as an alternative to the use of PMP for determination of OSPs. These are the cases of Evolutionary Algorithm (EA) [7][8][9]12,13], Ants Colony Optimization (ACO) [14,15], Particle Swarm Optimization (PSO) [16] and Simulated Annealing (SA) [17,18].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Real Time Train Energy Efficiency Optimization

et al. 2018

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Efficient use of energy is currently a very important issue. As conventional energy resources are limited, improving energy efficiency is, nowadays, present in any government policy. Railway systems consume a huge amount of energy, during normal operation, some routes working near maximum energy capacity. Therefore, maximizing energy efficiency in railway systems has, recently, received attention from railway operators, leading to research for new solutions that are able to reduce energy consumption without timetable constraints. In line with these goals, this paper proposes a Simulated Annealing optimization algorithm that minimizes train traction energy, constrained to existing timetable. For computational effort minimization, re-annealing is not used, the maximum number of iterations is one hundred, and generation of cruising and braking velocities is carefully made. A Matlab implementation of the Simulated Annealing optimization algorithm determines the best solution for the optimal speed profile between stations. It uses a dynamic model of the train for energy consumption calculations. Searching for optimal speed profile, as well as scheduling constraints, also uses line shape and velocity limits. As results are obtained in seconds, this new algorithm can be used as a real-time driver advisory system for energy saving and railway capacity increase. For now, a standalone version, with line data previously loaded, was developed. Comparison between algorithm results and real data, acquired in a railway line, proves its success. An implementation of the developed work as a connected driver advisory system, enabling scheduling and speed constraint updates in real time, is currently under development.

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“…Among these, ant colony optimization is based on the research of ants searching for food, which was proposed in the 1990s [31]. It is a heuristic search algorithm that has a successful application in solving the problems of path planning [3,20,32,33,34,35,36,37,38,39] and function optimization [40,41]. Ant colony optimization does well in positive feedback, parallelism, and robustness.…”

Section: Introductionmentioning

confidence: 99%

3D Path Planning for the Ground Robot with Improved Ant Colony Optimization

Wang

Kan

Guo

et al. 2019

Sensors

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Path planning is a fundamental issue in the aspect of robot navigation. As robots work in 3D environments, it is meaningful to study 3D path planning. To solve general problems of easily falling into local optimum and long search times in 3D path planning based on the ant colony algorithm, we proposed an improved the pheromone update and a heuristic function by introducing a safety value. We also designed two methods to calculate safety values. Concerning the path search, we designed a search mode combining the plane and visual fields and limited the search range of the robot. With regard to the deadlock problem, we adopted a 3D deadlock-free mechanism to enable ants to get out of the predicaments. With respect to simulations, we used a number of 3D terrains to carry out simulations and set different starting and end points in each terrain under the same external settings. According to the results of the improved ant colony algorithm and the basic ant colony algorithm, paths planned by the improved ant colony algorithm can effectively avoid obstacles, and their trajectories are smoother than that of the basic ant colony algorithm. The shortest path length is reduced by 8.164%, on average, compared with the results of the basic ant colony algorithm. We also compared the results of two methods for calculating safety values under the same terrain and external settings. Results show that by calculating the safety value in the environmental modeling stage in advance, and invoking the safety value directly in the path planning stage, the average running time is reduced by 91.56%, compared with calculating the safety value while path planning.

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Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology

Cited by 16 publications

References 28 publications

Energy‐optimal study integrated speed trajectories, timetable and the layout of neutral sections for high‐speed railway

Energy‐optimal study integrated speed trajectories, timetable and the layout of neutral sections for high‐speed railway

A New Approach for Real Time Train Energy Efficiency Optimization

3D Path Planning for the Ground Robot with Improved Ant Colony Optimization

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