Research On Optimization Of Automatic Train Operation For Saving Energy

With the social progress and development as well as the huge improvement on technologies of computer, communication and control, there has been an increasing demand for the safety, running time and riding comfort of a train. Therefore, people began to apply these technologies to the design of automatic train control system so as to replace the operation of the driver, realize automatic operation and achieve purposes of safety, punctuality, energy efficiency, precise positioning parking and comfort. On the basis of in-depth study on the structure, function and working principle of the automatic train control system, this paper begins with an analysis on the operating mode during the operation process of the train, along with the force conditions under various operating modes. After ascertaining the switching principle of the operating mode, the author conducts a study on the driving strategies and applies mathematical methods in the description of automatic train operation process, followed by the establishment of a mathematical model for the energy-saving and optimized control. Based on the data of CRH-2 and mathematical model, the author optimizes the running process of the train on the simulate route by the Particle Swarm Optimization Algorithm. The ATO target curve basically achieves the requirements of safety, punctuality, comfort and precise positioning parking, with a great cut-down on energy. In consideration of the actual conditions, the author presents the requirement on the performance of speed controller and designs a simulation software for ATO system by MATLAB. With Predictive Functional Control as the core algorithm, a speed controller of ATO system is designed and then compared with the speed controller designed by Generalized Predictive Control algorithm. The results show that the former speed controller is superior to the latter one in terms of robustness, stability and overshoot. Thereby, the feasibility of the application of Predictive Functional Control algorithm in the automatic train control system is validated.The research results indicate that the simulation software for ATO system designed by the above-mentioned methods can be applied to various routes and trains. It can also effectively reduce the impact of weather, road conditions and other uncertainties on train operations, achieving the targets of automatic train operation.