Conventional rail vehicles struggle to optimally satisfy the different suspension requirements for various track profiles, such as on a straight track with stochastic irregularities, curved track or switches and crossings, whereas mechatronically guided railway vehicles promise a large advantage over conventional vehicles in terms of reduced wheel–rail wear, improved guidance and opening new possibilities in vehicle architecture. Previous research in this area has looked into guidance and steering using multi-body simulation models of mechatronic rail vehicles of three different mechanical configurations – secondary yaw control, actuated solid-axle wheelset and driven independently rotating wheelsets (DIRW). The DIRW vehicle showed the best performance in terms of reduced wear and minimal flange contact and is therefore chosen in this paper for studying the behaviour of mechatronically guided rail vehicles on conventional switches and crossings. In the work presented here, a mechatronic vehicle with the DIRW configuration is run on moderate and high-speed track switches. The longer term motivation is to perform the switching function from on-board the vehicle as opposed to from the track as is done conventionally. As a first step towards this, the mechatronic vehicle model is compared against a conventional rail vehicle model on two track scenarios – a moderate speed C type switch and a high-speed H switch. A multi-body simulation software is used to produce a high fidelity model of an active rail vehicle with independently rotating wheelsets where each wheel has an integrated ‘wheelmotor’. This work demonstrates the theory that mechatronic rail vehicles could be used on conventional switches and crossings. The results show that the mechatronic vehicle gives a significant reduction in wear, reduced flange contact and improved ride quality on the through routes of both moderate and high-speed switches. On the diverging routes, the controller can be tuned to achieve minimal flange contact and improved ride quality at the expense of higher creep forces and wear.
Regions of extreme low-adhesion between the wheel and rail can cause critical problems in traction and braking. This can manifest in operational issues such as signals being passed at danger, or pessimistic network wide responses to mitigate for localised issues. Poor traction conditions can be caused by oil contaminants, rain, ice, condensation of water droplets (micro-wetting) or leaves on the line, where compressed leaf contamination can cause a rapid decrease in adhesion. The complexity of the problem arises as a result of the inability to directly measure and monitor all the factors involved. There remains a lack of real-time information regarding the state and location of low-adhesion areas across rail networks. On-board low adhesion detection technology installed to in-service vehicles is a suggested method to capture up-to-date adhesion information network wide and minimise significant disruptions and cancellations in railway schedules. This paper extends a principle of a model-based estimation technique previously developed in straight track running for operating in a curving scenario. The vehicle model of focus here will be a high-fidelity, multi-body physics representation of a full-vehicle.
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This paper proposes a new ultra-reliable architecture for fully electric propulsion primarily aimed at the automotive sector. The system considers a direct drive arrangement, minimizing the number of moving components and is based on a network of multi-three-phase machines providing propulsion. The DC-link from each 3-phase star within each machine can be interconnected to form a network, or mesh, which would allow a variety of post-fault mitigation strategies. The system scaling is considered along with the benefits and impact of modularity on device ratings. Finally the simulated performance for one drive is presented demonstrating the core capability in redirecting power flow in case of a fault.
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