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Reinforcement learning based urban rail train energy-saving operation strategy online optimization method

A technology of operation strategy and reinforcement learning, applied in transportation and packaging, railway car body parts, transportation center control system, etc., can solve the problem that the offline algorithm cannot actually disturb the online response, and achieve the effect of strong applicability

Active Publication Date: 2019-11-26
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

[0004] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides an online optimization method for energy-saving operation strategies of urban rail trains based on reinforcement learning, which solves the problem that existing offline algorithms cannot respond online to actual disturbances

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  • Reinforcement learning based urban rail train energy-saving operation strategy online optimization method
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  • Reinforcement learning based urban rail train energy-saving operation strategy online optimization method

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Embodiment Construction

[0050] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0051] Such as figure 1 As shown, an online optimization method for urban rail train energy-saving operation strategy based on reinforcement learning includes the following steps:

[0052] S1, determine the basic parameters of the train line section to be optimized;

[0053] The basic parameters of the train line section in step S1 include: train parameters, line parameters and operating parameters;

[0054] The train param...

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Abstract

The invention discloses a reinforcement learning based urban rail train energy-saving operation strategy online optimization method which comprises the following steps of firstly, analyzing a train operation process, establishing a multi-target speed adjustment model, and then performing solving on a train energy consumption optimization problem based on a reinforcement learning algorithm. In themethod, an energy-saving strategy can be selected for operation at different planned operation times and under the condition that safe, punctual, comfortable and accurate parking are satisfied by utilizing train speed and position information acquired in real time without a target speed curve, so that energy consumption is effectively lowered; and disturbance in the actual operation process can beresponded on line, and the method is strong in applicability.

Description

technical field [0001] The invention relates to the field of urban rail transit train operation control, in particular to an online optimization method for energy-saving operation strategies of urban rail trains based on reinforcement learning. Background technique [0002] In recent years, urban rail transit has developed rapidly due to its safety, comfort, high efficiency, and environmental protection. How to reduce the traction energy consumption of subway trains has become a research focus. The energy consumption of train traction largely depends on the operation strategy of the train. The classic energy consumption optimization problem of train operation is to know the characteristics of the train and the data of the operation line, and search offline to meet the constraints of overspeed protection, punctuality, comfort, and precise parking. The sequence of train operating conditions or the target speed curve with the lowest energy consumption. [0003] With the increa...

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Application Information

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IPC IPC(8): B61L27/00G06F17/50
CPCB61L27/00B61L27/40
Inventor 王小敏杨旭立张文芳
Owner SOUTHWEST JIAOTONG UNIV
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