Project description/objective

Railways are characterized by long routes and complex conditions. Furthermore, the relationship between driving strategies and energy consumption under different road conditions is highly complicated. How to obtain minimal energy consumption by optimizing driving speed of train has become a key concern of the railway bureau.

Challenges/pain points

The existing road simulations and optimal driving curves of the railway bureau are only based on experience and past data, and do not take into account the uncertainty of parking, changes in roads and locomotives. This project builds a theoretical model of diesel locomotive traction energy consumption, using actual driving data, and based on the analysis conclusions of traction energy consumption motivation, this model optimizes the driving strategy of diesel locomotives, and achieves more energy-saving effects.

Solution

On the basis of theoretical modeling and analysis, based on empirical data, the correlation between traction energy consumption and its influencing factors was described, and a traction energy consumption model of diesel locomotives, which includes physics and thermodynamic models, was established. The motivation analysis includes the influence of train weight resistance parameters, kinetic energy increment, wind resistance, etc. on traction energy consumption, and a new index of the train energy efficiency evaluation model is proposed. At the speed optimization level, the optimal speed solution strategy based on mathematical programming methods and particle swarm algorithm is proposed. The research results can provide energy-saving methods and theoretical basis for the performance evaluation model of the train group, the optimal train operation curve, and the operation organization of the existing line.

Key contribution/commercial implication

Take a DF4B train with a load of 1,635 tons as an example. It is known that the train started running from the mileage number 90.123 to 47.221 at 5:56 on September 1, 2019. A section of historical speed curve is also known. At the same time, the gradient parameters of this section of the road and the speed limit situation are known. After optimizing by the particle swarm algorithm, a total of 15.6% of the energy consumption of the traction motor was saved. The energy consumption motivation analysis model and the diesel locomotive operating speed curve optimization model based on the interval discrete model have been applied and deployed in related products of Guangdong Suguo Intelligent Company (the partner), and embedded in the platform for visual display. It has applied for an invention patent "Method, Device, Equipment and Storage Medium for Train Operation Optimization" and has entered the substantive examination stage. Application date: 2021.05.31. Application number: 202110598623.3

Next steps

continue to explore the feasibility of landing based on the research results

Collaborators/partners

Guangdong Suguo Technology Co., Ltd.; Guangzhou Railway Bureau Group

Team/contributors

Cai Xiaoqiang, Xu Lei, Huang Zili, Li Fengrong, Zhu Mingyuan