Abstract-Fuel economy of parallel hybrid electric vehicles is affected by both the torque split ratio and the vehicle velocity. To optimally schedule both variables, information about the surrounding traffic is necessary, but may be made available through telemetry. Consequently, in this paper, a nonlinear model predictive control algorithm is proposed for the vehicle control system to maximise fuel economy while satisfying constraints on battery state of charge, relative position and vehicle performance. Different scenarios are considered including allowing and disallowing overtaking; various hard and soft constraints; and computational aspects of the solution. The optimal control signal vector was found to be characterised by smooth changes in velocity and increases in the motor to engine power ratio as the vehicle accelerates. It was found that using feedforward information about traffic flow in the range of five to fifteen seconds has the potential for significant fuel savings over two urban drive cycles.

Prompted by technical issues that have arisen due to the widespread deployment of distributed intermittent renewable generators, rapidly rising peak demand and reductions in battery price, the use of battery‐based energy storage systems in power networks is on the rise. While battery‐based energy storage has the potential to deliver technical benefits, the best possible sizing, location and usage govern the financial viability. The objective of this study is twofold. Firstly, a generalised approach is proposed to model network upgrade deferral as a function of load growth rate, renewable generation penetration and peak shave fraction. This model is then used for the formulation of an optimisation problem which benefits from multi‐period power flow analysis to co‐optimise battery size, location, charge/discharge profile for a pre‐specified number of units to be deployed in a given distribution network. The proposed approach is implemented using the generic algebraic modelling system platform and validated on an Australian medium voltage distribution network under multiple practical and potential future scenarios.

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A new technique for fault diagnosis and estimation of unknown inputs in a class of nonlinear systems is presented in this paper. The novelty of the approach is based on utilization of a network of two interconnected sliding mode observers, the first is used for fault diagnosis and the second is used for estimation of unknown inputs. The two observers exchange information about their respective reconstructed signals online and in real time. Conditions and proofs of conversion are presented. A salient feature of the proposed approach is that the state trajectories do not leave the sliding manifold even in presence of unknown disturbances and faults. This allows for faults and unknown inputs to be reconstructed based on information retrieved from the equivalent output error injection signal.

The objective of this paper is to analyse reduction in wind power variability through aggregation and use of energy storage systems. A key focus is to evaluate the impact of regulatory framework in addition to the capital expenditure to ascertain techno-economic feasibility of energy storage systems in wind farm applications. A generic techno-economic is developed which takes into account the effects of regulatory framework in addition to the technical and economic features of storage options. Existing wind farms from South Australia are used as test cases. First, a detailed quantitative analysis is performed to establish the variability associated with individual wind farms and the aggregations of their power outputs. Then, the appropriateness of a number of existing energy storage types are evaluated using the developed techno-economic model. Relationships between wind farm sizes, wind farm variability levels, storage capacity requirements, storage costs and storage payback times are determined and discussed for both current and potential future economic and regulatory scenarios. It is found that regulatory framework can be of paramount importance in ascertaining the economic feasibility of energy storage. For example, if the ramp-rate violation penalty (determined to be $8.89/MW/min) is doubled, then the payback time of energy storage capital investment is found to reduce from 5.32 years to 2.52 years. It is also found that larger wind farms require smaller energy storage capacity and smaller wind farms generally results in a shorter energy storage system payback times.

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Hybrid and fully electric vehicles are becoming more common as a response to rising fuel prices and greenhouse considerations. While the benefits of electrification on urban air quality have been studied quite widely, financial assessments of the various alternative vehicle forms are less common, particularly for Australian driving conditions. The aim of this paper is therefore to identify the scenarios under which different vehicle configurations are attractive to the vehicle owner. A Class-E conventional vehicle is compared with full-electric, plug-in hybrid, parallel hybrid, series hybrid and mild hybrid electric vehicle configurations. A simulation model of a conventional internal combustion engine based large sized car is developed and validated against experimental data. The conventional vehicle model is then systematically altered to obtain its increasingly electric variants. The fuel consumption and greenhouse gas emissions are simulated on the legislative NEDC drive cycle and the more representative Australian Urban Drive Cycle (AUDC). The outcomes of these tests are used to estimate the total cost of ownership and in-service emissions, thus allowing the cost of emissions mitigation to be approximated for the different vehicles. Different scenarios are considered for the pricing of energy and major powertrain components. This provides a baseline assessment based on current prices and projections, as well as 'electrification favorable' and 'electrification unfavorable' scenarios. The impact on vehicle emissions of significant penetration of renewable energy into the Australian electricity grid is also considered.