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This paper proposes a hierarchical optimization energy management strategy (EMS) considering speed profile to explore energy-saving potential and achieve reasonable power distribution for the hybrid propulsion system of a tramway composed of the catenary, a battery pack and an ultracapacitor pack. For the higher layer, a speed profile is optimized in the space domain through a particle swarm optimization algorithm, which balances energy consumption, running time and passenger comfort. For the lower layer, based on the optimized speed profile, an EMS is designed, using dynamic programming. The boundary range calculation of state variables for the dynamic programming is carried out in advance, based on the working mode analysis to reduce the computation load. In addition, a supervisory controller is developed to realise real-time control. Finally, to verify the proposed method, real rail line data in China is used as a case study, and the results show that the proposed method offers better performance.

The wheelset treads and axle box bearings of railway vehicles often suffer from fatigue failures. Their regular maintenance highly depends on manual off-line inspection with low working efficiency and poor precision for early failure detection. This study proposes a fault diagnosis method by band-pass filtering and by enveloping the accelerations collected from the axle box bearing on the underfloor wheelset lathe to improve the maintenance efficiency. This process is followed by the refined phase difference correction using the four-term third derivative Nuttall-windowed fast Fourier transform (RPNWF) to extract accurate amplitudes of the fault characteristic frequency and its harmonics. The integration scheme, work flow, and application examples of the fault diagnosis system are presented. Simulation analysis and results show that the developed method can achieve effective diagnosis of the fault and fault degree of axle box bearings as well as yield better correction accuracy than the commonly used discrete spectrum correction methods.

A railway vehicle’s key components, such as wheelset treads and axle box bearings, often suffer from fatigue failures. If these faults are not detected and dealt with in time, the running safety of the railway vehicle will be seriously affected. To detect these components’ early failure and extend their fatigue life, a regular maintenance becomes critical. Currently, the regular maintenance of axle box bearings mainly depends on manual off-line inspection, which has low working efficiency and precision of fault diagnosis. In order to improve the maintenance efficiency and effectiveness of railway vehicles, this study proposes a method of integrating the vibration monitoring system of the axle box bearing in the underfloor wheelset lathe, where the integration scheme and work flow of the system are introduced followed by the detailed fault diagnosis method and application examples. Firstly, the band-pass filter and envelope analysis is successively performed on the original signal acquired by an accelerometer. Secondly, the alpha-stable distribution (ASD) and multifractal detrended fluctuation analysis (MFDFA) analysis of the envelope signal are performed, and five characteristic parameters with significant stability and sensitivity are extracted and then brought into the least squares support vectors machine based on particle swarm optimization to determine the state of the bearing quantitatively. Finally, the effectiveness of the method is validated by bench test data. The results demonstrated that the proposed method can accomplish effective diagnosis of axle box bearings’ fault location and fault degree and can yield better diagnosis accuracy than the single method of ASD or MFDFA.

Automatic mechanical transmission (AMT) is now widely used in electric vehicle (EV) to improve its performance. In this chapter different control strategies are proposed to deal with the characteristics of electric vehicle AMT system and its different working condition. And then the control strategy including shift schedule, driver’s intention interpretation, driving cycle recognition and frequent gearshift suppression was systematically researched and modeled with SIMULINK. After verified by Model-In-Loop (MIL) and Hardware-In-Loop (HIL) tests, the control strategy model was interpreted to C codes and used in real control unit, verified in vehicle test. Test results show that this control strategy’s performance can meet with the requirements of the AMT control system.