A review of hydrogen technologies and engineering solutions for railway vehicle design and operations

Interest in hydrogen-powered rail vehicles has gradually increased worldwide over recent decades due to the global pressure on reduction in greenhouse gas emissions, technology availability, and multiple options of power supply. In the past, research and development have been primarily focusing on light rail and regional trains, but the interest in hydrogen-powered freight and heavy haul trains is also growing. The review shows that some technical feasibility has been demonstrated from the research and experiments on proof-of-concept designs. Several rail vehicles powered by hydrogen either are currently operating or are the subject of experimental programmes. The paper identifies that fuel cell technology is well developed and has obvious application in providing electrical traction power, while hydrogen combustion in traditional IC engines and gas turbines is not yet well developed. The need for on-board energy storage is discussed along with the benefits of energy management and control systems.

An AdaBoost-Based Intelligent Driving Algorithm for Heavy-Haul Trains

Heavy-haul trains have the characteristics of large volume, long formation, and complex line conditions, which increase the driving difficulty of drivers and can easily cause safety problems. In order to improve the safety and efficiency of heavy-haul railways, the train control mode urgently needs to be developed towards the direction of automatic driving. In this paper, we take the Shuohuang Railway as the research background and analyze the train operation data of SS4G locomotives. We find that the proportion of operation data under different working conditions is seriously out of balance. Aiming at this unbalanced characteristic, we introduce the classification method in the field of machine learning and design an intelligent driving algorithm for heavy-haul trains. Specifically, we extract the data by random forest algorithm and compare the classification performance of C4.5 and CART algorithms. We then select the CART algorithm as the base classifier of the AdaBoost algorithm to build the model of the automatic air brake. For the purpose of heightening the precision of the model, we optimize the AdaBoost algorithm by improving the generation of training subsets and the weight of voting. The numerical results certify the effectiveness of our proposed approach.

Experimental assessment of the dynamic performance of slave control locomotive couplers in 20,000-tonne heavy-haul trains

Coupler jack-knifing and derailment of slave control locomotives in 20,000-tonne heavy-haul trains usually occur due to the combinations of large in-train forces with poor track conditions. This paper presents an experimental investigation on the dynamic performance of slave control locomotive couplers in 20,000-tonne heavy-haul trains. A field test method for measuring and monitoring the dynamic behavior of heavy-haul locomotive couplers is proposed. The on-track tests involved coupler forces, coupler yaw angles, and coupler dynamic motion monitoring of the test locomotive in the whole traveling process. The dynamic process of coupler jack-knifing is exhibited, and the distribution characteristics of the coupler forces and yaw angles are investigated. The test results indicate that the front and rear couplers of the slave locomotive group respectively suffer the largest draw (tensile) and buffing (compressive) forces, and the maximum yaw angle appears at the middle coupler between the slave locomotives. The maximum draw and buffing forces attain 1551 kN and 2067 kN, respectively. The maximum absolute yaw angle is about 13 degrees. During the cycle braking operation conditions, the intense buffing forces and the excessive coupler yaw angles are more likely to appear in the release process. The coupler instability generally occurs under train run in buffing forces. Moreover, the coupler status, the track conditions as well as the duration of buffing forces are also supposed to have an important effect on the coupler stability of slave control locomotives.

Tracking control via sliding mode for heavy-haul trains with input saturation

To address the tracking control problem of heavy-haul trains (HHTs) with input saturation during operation, an anti-saturation sliding mode (SMES) control method based on dynamic auxiliary compensator (DAC) is presented. Firstly, an HHT model with nonlinear coupling and uncertain disturbances is built. Secondly, a new type of DAC is introduced to overcome the difficulty of traditional dynamic auxiliary compensator (TDAC) with a large upper bound on the compensation signal. Finally, an anti-saturation SMES control algorithm is designed to reduce the influence of input saturation on the tracking accuracy of each carriage. Simulation results verify the effectiveness of the algorithm in terms of tracking accuracy, anti-interference, and anti-saturation.