East China Jiaotong University

Crsc Research & Design Institute Group Co., Ltd.

Yanli Zhou

Hui Yang

Qi Wang

Ling Liu

Zhongqi Li

Junfeng Cui

Miao Yan

Kunpeng Zhang

Luojun Yang

Changyuan Wang

A cooperative control method and system for electro-hydraulic hybrid braking of a middle-low speed maglev train is provided, which relates to the field of vehicle braking control. The method includes: denoising operation data of a middle-low speed maglev train; using a controlled autoregressive integrated moving average model as an electro-hydraulic hybrid braking process model for the middle-low speed maglev train, and processing denoised operation data by using a least square method to determine parameters in the controlled autoregressive integrated moving average model; establishing a generalized predictive control model with time lag compensation according to the controlled autoregressive integrated moving average model and a Smith predictor; and performing cooperative control on an electro-hydraulic hybrid braking process of the middle-low speed maglev train by using the generalized predictive control model with time lag compensation. A time lag in the electro-hydraulic hybrid braking process of the middle-low speed maglev train is reduced; control accuracy of the electro-hydraulic hybrid braking process of the middle-low speed maglev train is improved to a certain extent; and a speed tracking effect is improved.

Cooperative control method for electro-hydraulic hybrid braking of middle-low speed maglev train

Disclosed are a method and a system for multi-objective optimization of urban train operation. Firstly, speed limit information, slope information and curve radius information of a real train route are obtained, a section is segmented into non-equal sub-sections according to the above information about actual line characteristics, and then a longitudinal dynamics model of the train is constructed in combination with basic vehicle data of the train. Next, energy consumption of the train operation, section operation time, actual parking positions, and rates of acceleration change are calculated, so as to construct a multi-objective optimization model of train operation. Afterwards, a multi-objective differential evolution algorithm is used to solve the multi-objective optimization model, in order to obtain a Pareto optimal solution set of each operation district. Finally, an optimal solution is obtained which takes all objectives into comprehensive consideration, and an optimal train speed curve is generated.

Method and system for multi-objective optimization of urban train operation

Growing community of inventors

Nanchang, China

Yanli Zhou