Abobghala, Abdelmenem (2018) ASSESSING THE ENERGY EFFICIENCY OF RAILWAY VEHICLES WITH WHEELSET ACTIVE CONTROL. Doctoral thesis, University of Huddersfield.
Abstract

Energy consumption in electric locomotives is principally the power consumed in traction motors. In order to reduce this energy consumption, the motion resistances of the train need to be reduced. These resistances include aerodynamics; inertial and grade forces; curving resistance; and bearing and wheel/rail friction.

Though many factors such as gradient resistance cannot be modified, if a control system is included, curving resistance can be minimised by reducing the energy losses in the contact patches between wheel and rail. Therefore, operational practices could be modified in order to obtain the most appropriate wheelset attack angle between wheel and rail, and appropriate train speed. One solution is to implement a steering control system. The function of this control system is to monitor and control the wheelset lateral displacement or the attack angle of the wheelset. This could reduce the energy dissipated at the contact points between wheel and rail, consequently reducing the energy consumed by traction motors in railway vehicles.

Therefore, the work presented in this thesis aims to design and develop a control method for combined vehicle traction and wheelset active steering control systems and to assess the energy efficiency of a rail vehicle under typical operational conditions.

In order to achieve these aims, two dynamic models of a typical railway vehicle have been developed in MATLAB and Simulink. The first model comprises the electrical traction and mechanical system passive system). The second model includes the passive and the wheelset active steering control system (active system). These models are used to determine the relationship between traction energy consumption and the energy dissipated in the contact points between wheel and rail, and to compare the passive steering system to the wheelset active steering control system, determining the possibilities for energy saving.

In order to assess the influence of the wheelset active steering control on the relationship between wheel and rail contact forces and traction power a series of deterministic track features are set comprising curve radii with different cant deficiencies and wheel conicities. Also a typical track profile from Leeds to Hull is used. From these simulations, the traction energy consumption, energy dissipated at the contact patches, and energy consumed by the steering actuators are calculated.

Statistical analyses are used to understand the relationship between the traction power and wheelset motion dynamics (lateral displacement and attack angle). The active vehicle model scheme is used to investigate the improvement of the energy efficiency of a railway vehicle using active steering. The wheelset active steering control system analysis shows whether different combinations of vehicle speed, wheelset conicity and track curve radius lead to a reduction, no reduction, or an increase intraction power consumption. The probability of high power consumption under different conditions is assessed to ensure that it is reduced wherever possible.

The ability of a forecasting model to predict the traction power consumption behaviour of railway vehicles from the wheelset motion dynamic is assessed. Findings show that the overall prediction accuracy is fairly similar to the power measured from the passive vehicle running on a track from Leeds to Hull. However, the algorithm does not perform effectively for the deterministic track features.

Finally, the benefits of implementing wheelset active steering control systems in terms of the mitigation of contact forces between wheels and rails and how this mitigation influences traction energy consumption are evaluated to determine under what conditions energy can be saved.

Information
Library
Documents
[thumbnail of Abobghala THESIS.pdf]
Preview
Abobghala THESIS.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (8MB) | Preview
Statistics

Downloads

Downloads per month over past year

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email