On-time and energy-saving train operation strategy based on improved AGA multi-objective optimization

He, Jing; Qiao, Duo; Zhang, Changfan

doi:10.1177/09544097231203271

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…where the maximum electric brake force u d max is a piecewise function related to the operating speed [22], and the air braking force u a is a function of the air pressure drop [23].…”

Section: Running Constraintsmentioning

confidence: 99%

Cyclic Air Braking Strategy for Heavy Haul Trains on Long Downhill Sections Based on Q-Learning Algorithm

Zhang,

Zhou,

et al. 2024

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Cyclic air braking is a key factor affecting the safe operation of trains on long downhill sections. However, a train’s cycle braking strategy is constrained by multiple factors such as driving environment, speed, and air-refilling time. A Q-learning algorithm-based cyclic braking strategy for a heavy haul train on long downhill sections is proposed to address this challenge. First, the operating environment of a heavy haul train on long downhill sections is designed, considering various constraint parameters, such as the characteristics of special operating routes, allowable operating speeds, and train tube air-refilling time. Second, the operating status and braking operation of a heavy haul train on long downhill sections are discretized in order to establish a Q-table based on state–action pairs. The training of algorithm performance is achieved by continuously updating Q-tables. Finally, taking the heavy haul train formation as the study object, actual line data from the Shuozhou–Huanghua Railway are used for experimental simulation, and different hyperparameters and entry speed conditions are considered. The results show that the safe and stable cyclic braking of a heavy haul train on long downhill sections is achieved. The effectiveness of the Q-learning control strategy is verified.

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“…where the maximum electric brake force u d max is a piecewise function related to the operating speed [22], and the air braking force u a is a function of the air pressure drop [23].…”

Section: Running Constraintsmentioning

confidence: 99%

Cyclic Air Braking Strategy for Heavy Haul Trains on Long Downhill Sections Based on Q-Learning Algorithm

Zhang,

Zhou,

et al. 2024

Information

Self Cite

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“…and 01 f   , 3 0   and 0 b  = according to ( 7), ( 8) and (10). In this condition, the speed is held at a certain value () v x V = .…”

Section:  =mentioning

confidence: 99%

“…To solve the TTO problem, many scholars have conducted extensive research on train operation control strategies and control methods, which are mainly divided into five categories: Pontryagin's maximum principle (PMP) [3][4][5][6][7], quadratic programming (QP) [8,9], heuristic method [10][11][12][13], dynamic programming (DP) [14,15] and Reinforcement learning (RL). the PMP faces significant challenges in TTO problems, especially when dealing with hard constraints on non-flat and mutil speed-limited tracks, it is difficult to find the optimal conversion conditions.…”

Section: Introductionmentioning

confidence: 99%

A train trajectory optimization method based on the safety reinforcement learning with a relaxed dynamic reward

Cheng,

Cao,

Yang

et al. 2024

Preprint

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Train trajectory optimization (TTO) is an effective way to address energy consumption in rail transit. Reinforcement learning (RL), an excellent optimization method, has been used to solve TTO problems. Although traditional RL algorithms use penalty functions to restrict the random exploration behavior of agents, they cannot fully guarantee the safety of the process and results. This paper proposes a proximal policy optimization based safety reinforcement learning framework (S-PPO) for the train trajectory optimization, including a safe action rechoosing mechanism (SARM) and a relaxed dynamic reward mechanism (RDRM) combining a relaxed sparse reward and a dynamic dense reward. SARM guarantees that the new states generated by the agent consistently adhere to the environmental security constraints, thereby enhancing sampling efficiency and facilitating algorithm convergence. RDRM is composed of a relaxed sparse reward and a dynamic dense reward, offering a better balance between exploration and exploitation. The experimental results show that S-PPO can significantly improve the exploration ability of the algorithm, obtain better train operation trajectories than soft constraint algorithms, and the convergence process is smoother. Finally, it was demonstrated that S-PPO exhibits good adaptability across various speed limit tracks.

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On-time and energy-saving train operation strategy based on improved AGA multi-objective optimization

Cited by 2 publications

References 23 publications

Cyclic Air Braking Strategy for Heavy Haul Trains on Long Downhill Sections Based on Q-Learning Algorithm

Cyclic Air Braking Strategy for Heavy Haul Trains on Long Downhill Sections Based on Q-Learning Algorithm

A train trajectory optimization method based on the safety reinforcement learning with a relaxed dynamic reward

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