Research on the driving strategy of heavy-haul train based on improved genetic algorithm

Huang, Youneng; Bai, Shuai; Meng, Xianhong; Yu, Huazhen; Wang, Mingzhu

doi:10.1177/1687814018791016

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…As a result, the force deviation caused by train length and the power transmission between different vehicles are considered in the model. However, this model has a complicated modeling process and is also difficult to calculate [35]. Due to the lighter weight and shorter length of the metros, this accuracy is not necessary.…”

Section: Dynamic Characteristics Of Metro Trainsmentioning

confidence: 99%

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Energy-Efficient Speed Profile Optimization and Sliding Mode Speed Tracking for Metros

et al. 2020

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Nowadays, most metro vehicles are equipped with an automatic train operation (ATO) system, and the speed control method, combining cruise speed planning and proportional-integral-derivative (PID) control, is widely used. The automation is achieved, and the energy-efficient can be improved. This paper presents an improved artificial bee colony algorithm for speed profile optimization with coast mode and an adaptive terminal sliding mode method for speed tracking. Specifically, a multi-objective optimization model is established, which considers energy consumption, comfortableness, and punctuality. Then, a novel artificial bee colony algorithm named regional reinforcement artificial bee colony (RR-ABC) is designed, to search the optimal speed profile with coast mode, in which some improvements are made to speed up convergence and to avoid local optimal solutions. For speed-tracking control, the adaptive terminal sliding mode controller (ATSMC) is used to improve the speed error, robustness, and energy saving. In addition, a disturbance observer (DOB) is designed to improve the anti-interference ability of the system and further improve the robustness and anti-disturbance, which are also conducive to speed error and energy saving. Finally, the line and train data of the Qingdao Metro Line 6 are used for simulation, which proves the effectiveness of the study. Specific to the energy saving rate, and compared with normal algorithms, RR-ABC with coast mode is approximately 9.55%, and ATSMC+DOB is 7.58%.

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Section: Dynamic Characteristics Of Metro Trainsmentioning

confidence: 99%

“…where e 1 is the train position error, e 2 is the train speed error, x r is the reference position of optimized profile, and v r is the reference speed of optimized profile. Differentiate Equation (35) and substitute it into Equation 31, and the resulting error state space Equation is as follows:…”

Section: Design Of Sliding Mode Terminal Controllermentioning

confidence: 99%

Energy-Efficient Speed Profile Optimization and Sliding Mode Speed Tracking for Metros

et al. 2020

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“…23,24 Empirical models have been reported as computationally efficient as well as effective. [6][7][8][9][10][11][12] For longitudinal train dynamics simulations, results required from air brake system models are brake forces, therefore brake pipe models are not required, which also makes empirical models justified. In addition, modern intelligent transport systems often require fast models to predict system performance and to adjust train control actions.…”

Section: Introductionmentioning

confidence: 99%

“…Example of such models can be found in Refs. [7][8][9][10][11][12] Understandably, they did not explicitly model fluid dynamics behaviour of brake pipes and brake valves. Brake behaviour was simulated by fitting the time variations of pressure distributions among the studied brake cylinders.…”

Section: Introductionmentioning

confidence: 99%

An experiment-based empirical model for heavy-haul train air brake

Fan¹,

Li²,

et al. 2023

Advances in Mechanical Engineering

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The latest international survey shows that slow computational speed is still a significant issue for air brake models. Fluid dynamics air brake models can be more accurate but are slower in computing speed. Empirical models are reported as effective and more efficient. This article developed a new experimental based empirical air brake model that is more comprehensive and can be used to simulate both pneumatically and electronically controlled air brake systems, as well as locomotive air brake systems and brake systems for radio-based distributed power trains. Multiple functions were used to simulate various characteristics of brake cylinder pressures, which enables the model to capture more details. An activating algorithm was developed to further improve the computational efficiency. Case studies were conducted to compare the simulation results with experimental data. Simulation results and experimental data had good agreements regarding brake delays, force patterns and cylinder pressure amplitudes. The empirical air brake model is about 70 times faster than a fluid dynamics model and 7.4 times faster than real-time.

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“…Huang et al. 11 optimised train driving strategies by using a Genetic Algorithm to increase train speeds and limit in-train forces to acceptable levels. Wei and Wang 12 studied the implications of various driving strategies for in-train forces while a heavy haul train was negotiating a section of undulating track.…”

Section: Introductionmentioning

confidence: 99%

Hill-starting a heavy haul train with a 24-axle locomotive

Yang²,

Jin³

2021

Proceedings of the Institution of Mechanical Engineers, Part F:

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Head-end trains are more efficient than Distributed Power trains in terms of train marshalling. This paper studied an unprecedent real-world engineering problem to hill-start a long heavy haul train at a 1.2% up-hill gradient by using a 14400 kW 24-axle AC electric locomotive (maximum traction force 2280 kN). Longitudinal Train Dynamics simulation were conducted to assess five potential driving strategies in terms of train speed and in-train forces. The simulation results show that the locomotive can hill-start the heavy haul train with 80% traction throttle. However, maximum locomotive coupler forces (1840 kN) exceeded coupler knuckle yield strength (1780 kN) of the original coupler system design. Sensitivity analyses were conducted to assess the implications of train starting resistance for train speeds and in-train forces. The study recommends increasing coupler knuckle yield strength to 2450 kN. The increased knuckle yield strength is still lower than that of the chassis, which is able to meet system design requirement; it also enables the locomotive to hill-start by using traction throttles of 80, 90, and 100% with safety factors of 1.3, 1.2 and 1.1 respectively.

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Research on the driving strategy of heavy-haul train based on improved genetic algorithm

Cited by 14 publications

References 22 publications

Energy-Efficient Speed Profile Optimization and Sliding Mode Speed Tracking for Metros

Energy-Efficient Speed Profile Optimization and Sliding Mode Speed Tracking for Metros

An experiment-based empirical model for heavy-haul train air brake

Hill-starting a heavy haul train with a 24-axle locomotive

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