Alasdair Renfrew

Modeling Electrified Transit Systems

This paper introduces a new modeling method for light rail traction to allow a detailed drive model to be incorporated into the tram system, thereby permitting detailed energy flows to be studied. Using movement data as an input, this method is shown to be highly suitable for cases employing real measured telemetry to compare the efficiencies of light-rail vehicle designs. Data from the Blackpool tram network in the United Kingdom are used throughout.

Simplified Power Converter for Integrated Traction Energy Storage

Electrical energy storage has a significant role to play in improving the performance of future electric traction systems. This paper proposes a new power electronics topology that integrates the energy storage power electronics with those of the inverter drive system. This topology reduces weight and component count compared with previous topologies but still allows the use of standard machines. Practical results from a laboratory test system are shown, and indicative energy savings for a full-sized system are presented. A study on a City Class Tram on the public transportation system in Blackpool, U.K., shows that clear energy savings may be made by employing ultracapacitor energy storage with the proposed topology.

Developing a strategy for student retention

Attracting and retaining high-calibre students in science and engineering is becoming more difficult. At the same time, we are urged by the UK Government to recruit from a wider pool. This has necessitated a review of how we go about recruiting and retaining our students. This paper sets out some of our underpinning ideas. Student retention needs can be focussed on a number of stages. First, school-level motivation, starting at secondary, and probably even primary school level. Second, recruitment of students of the appropriate calibre, motivation and experience. Third, student induction, including an overview of subject and programme. Fourth, ongoing student support.

A controller design methodology for close headway spacing strategies for automated vehicles

An extended analysis of a vehicle following at close headway is carried out. A largely linear model of a vehicle is used to permit the computation of a large convoy. Vehicular interactions within a convoy in which each vehicle receives only information on the status of the preceding vehicle are analysed and a matrix representation of the set of interactions is developed, the main emphasis being on the propagation of spacing errors within the convoy. The conditions for positive impulse response, attenuation of error and stable, non-oscillatory response are identified in terms of both pole placement and volume space in which stable operating conditions may be located. The implications of the choice of controller parameters are studied with four examples and related to the information flow strategy and requirements within each convoy. The paper then presents the proposed short headway controller design methodology.

Four Case Studies of Adapting Enquiry-Based Learning (EBL) in Electrical and Electronic Engineering

Four different instances of enquiry-based learning (EBL), developed in a School of Electrical and Electronic Engineering, are described. Key decisions in the design of these activities are detailed, emphasising flexibility in approach. Although the activities took place in broadly the same environment, the local contexts required subtle tailoring. The design decisions taken in each case are described and general overviews from integrative evaluations are provided. An emergent distinction between the forms of EBL developed was between those that focused on generic or specific skills and those that focused on content knowledge; these may be termed project-based learning and problem-based learning respectively. The influence of the focus of the activity on the design decisions is described.

Novel optical flow optimization using pulse-coupled neural network and smallest univalue segment assimilating nucleus

Optical flow, with abundant local motion information, has been widely investigated in the last few decades. To improve the robustness and accuracy of optical flow estimation, we proposed a systematic optimization algorithm based on the pulse-coupled neural network (PCNN) and the smallest univalue segment assimilating nucleus (SUSAN). The primary aim of the proposed algorithm is to overcome the problems incurred by the noise disturbance and texture insufficiency. Specifically, our method performs a homogeneous area extraction based on texture variation using a nonlinear filter, smallest univalue segment assimilating nucleus. Then a novel 3-D pulse- coupled neural network model is constructed to perform optical flow optimization. Because of the excellent clustering capability of the PCNN, the proposed algorithm significantly improves the quality of optical flow estimation in the presence of noise. The enhanced optical flow field and extraction results are combined to solve the problem of insufficient texture. The proposed algorithm is evaluated in both synthetic and real testing images to demonstrate its excellent performance.

Application of the “Contact Convoy” Concept to Hybrid Electric Vehicles

This paper applies an intelligent intervehicle device (termed a ldquobridging damperrdquo) to a convoy of in-contact vehicles. The field of application is backup for future automated highways and operation of novel guided systems using vehicle-following control. The vehicle represented in computer simulations is a parallel hybrid electric automobile. Using the advanced vehicle simulator (ADVISOR) model, the vehicle representation is tuned to achieve matching of the overall force and power characteristics and the same performance over an urban driving cycle. The model for which a dedicated Matlab Toolbox was constructed is described. Simulation results compare the vehicle-following control on its own with a passive damper and a ldquoquasi-activerdquo damper that is controlled in collaboration with the vehicles drivetrain. Using transient disturbances in the form of wind gusts and short gradients, power peaks and longitudinal ride quality (vibration dose value) are used to evaluate the performance.

Vehicle Ego-Motion Estimation by using Pulse-Coupled Neural Network

This paper presents a visual odometer system using a monocular camera for vehicle navigation. A novel algorithm for vehicle ego-motion estimation based on optical flow and image segmentation is proposed. By applying a pulse-coupled neural network (PCNN), the image is dynamically divided into road area and non-road area by analysing texture smoothness. Correct road region detection effectively reduces computation cost and improves accuracy of ego-motion estimation. Then a novel optical flow optimization method is proposed to produce reliable optical flow field in the road area detected previously. Its known when the vehicle is moving on a planar structured road, its 2D motion field is expected to have specific form. Therefore ego-motion of vehicle, instantaneous speed and angular velocity, can be recovered from optical flow field of road area. Experiments show that the visual odometer successfully provides driver with robust and accurate vehicle self motion information.

Parameter Estimation Selection for a Sensorless Railway Traction application

This paper presents an approach to solving the speed estimation problem in high-power railway traction applications, including the very low speed range. The full-order Luenberger observer design, based on voltage and current models, is presented as the optimal alternative instead of the conventional method based on observer pole placement. Observer discretization and parameter sensitivity is also analyzed. An adaptive scheme based on Lyapunov theory is proposed for the on-line estimation of stator resistance and the motor speed considering the mismatch of the parameters, measurement noise and inverter model errors. Rotor resistance is also compensated for improved robustness

Analysis of Energy Dissipation in an Electric Transit System

Reducing energy consumption in electrified transit systems is becoming increasingly important as energy prices rise and environmental concerns become more prominent. In electrified transit systems, significant savings can be accomplished by utilizing braking energy. Energy from regenerative braking can be used to power vehicles via the traction supply system, or stored on board vehicles using energy storage devices. The effectiveness and energy savings of regenerative braking or on board energy storage is dependent on the nature of the system, particularly the vehicle drive cycles and service frequency. The paper describes an analysis methodology developed to consider energy flows in an electrified transit system, hence enabling the potential benefits of regenerative braking and energy storage devices to be assessed.Copyright