Search Results

This paper presents a technical and financial analysis of several, potentially near-zero greenhouse gas emission passenger vehicles for Australian driving conditions. Conventional, series hybrid, plug-in hybrid (PHEV) and fully electric (BEV) vehicles of class B and class E sizes are considered, with their propulsive energy assumed to originate from a source that is free of net greenhouse gas emissions. Extensions to the vehicle models developed by the authors in our previous works [1, 2, 3] are first developed. These enable estimation of the size of each major component in each powertrain, and therefore the total, in-service energy consumption and in-service greenhouse gas emissions. The component sizing also allows estimation of the each vehicle's purchase price, its embodied energy and its embodied greenhouse gas emissions, the latter assuming scenarios for both the current and a future, low emission intensity of Australian manufacturing.

Geometric imperfections on brake rotor surface are well-known for causing periodic variations in brake torque during braking. This leads to brake judder, where vibrations are felt in the brake pedal, vehicle floor and/or steering wheel. Existing solutions to address judder often involve multiple phases of component design, extensive testing and improvement of manufacturing procedures, leading to the increase in development cost. To address this issue, active brake torque variation (BTV) compensation has been proposed for an electromechanical brake (EMB). The proposed compensator takes advantage of the EMB's powerful actuator, reasonably rigid transmission unit and high bandwidth tracking performance in achieving judder reduction.

In the literature, a wide range of Mean Value Engine Models are used in the simulation and control of reciprocating engines. These models are often underpinned by a number of implicit assumptions, which determine the model structure and system states. Systematic model reduction approaches have been developed to avoid these assumptions, where high order models are reduced using singular perturbation techniques, eliminating states operating on irrelevant time-scales. While this framework allows the elimination of states based on sufficiently small perturbation parameters, a systematic method of identifying non-dimensional perturbation parameters has not yet been proposed. The development of a rigorous method to identify non-dimensional time scales present in the model is a natural and powerful extension to the existing approach.