With increased need for high power density, high efficiency and high temperature capabilities in aerospace and automotive applications, integrated motor drives (IMD) offers a potential solution. However, close physical integration of the converter and the machine may also lead to an increase in components temperature. This requires careful mechanical, structural and thermal analysis; and design of the IMD system. This study reviews existing IMD technologies and their thermal effects on the IMD system. The effects of the power electronics position on the IMD system and its respective thermal management concepts are also investigated. The challenges faced in designing and manufacturing of an IMD along with the mechanical and structural impacts of close physical integration is also discussed and potential solutions are provided. Potential converter topologies for an IMD like the matrix converter, two-level bridge, three-level neutral point clamped and multiphase full bridge converters are also reviewed. Wide band gap devices like silicon carbide and gallium nitride and their packaging in power modules for IMDs are also discussed. Power modules components and packaging technologies are also presented.
Abstract-This paper investigates the effect of two soft magnetic materials on a high speed machine design, namely 6.5% Silicon Steel and Cobalt-Iron alloy. The effect of design parameters on the machine performance as an aircraft starter-generator is analysed. The material properties which include B-H characteristics and the losses are obtained at different frequencies under an experiment and used to predict the machine performance accurately. In the investigation presented in this paper, it is shown that machines designed with 6.5% Silicon Steel at a high core flux density has lower weight and lower losses than the Cobalt-Iron alloy designs. This is mainly due to the extra weight contributed by the copper content especially in the end-windings. Due to the high operating frequencies, the core-losses in the Cobalt-Iron machine designs are found to outweigh the copper-losses incurred in the Silicon Steel machines. It is also shown that change in stack length/number of turns has a considerable effect on the copper losses at starting, however has no significant advantage on rated efficiency which happens to be in a field-weakening operating point. It is also shown that the performance of the machine designs depend significantly on material selection and the operating point of the core. The implications of the variation of design parameters on the machine performance is discussed and provides insight into the influence of parameters that effect overall power density.Index Terms-Aircraft, cobalt steel, flux density, high speed, machine design, soft magnetic material, silicon steel
For a continuous mode of operation, insulating material in an electrical machine is subject to constant thermal, electrical, mechanical and environmental stresses where thermal stress is a major cause of gradual insulation deterioration, which leads to ultimate winding failure. To guarantee a satisfactory lifetime, electrical machines are designed to operate winding temperatures well below their thermal class, which results in an oversized design. Standard methods for thermal lifetime evaluation of electrical machines are based on accelerated aging tests that require several months of testing. This paper proposes an alternative approach relying on a supervised neural network that significantly shortens the time demanded by accelerated aging tests for thermal lifetime evaluation of electrical machines. The supervised neural network is based on a feedforward neural network trained with Bayesian Regularisation Backpropagation (BRP) algorithm. The network predicts the wire insulation resistance with respect to its aging time at aging temperatures of 250 • C, 270 • C and 290 • C, which reveals a good match of prediction outcomes against the experimental findings. The mean time-to-failure at each aging temperature is extracted using the Weibull probability plot in order to compare the Arrhenius curves for both conventional and proposed method and a relative error of 0.125% is achieved in terms of their temperature indexes. In addition, the analysis shows a time saving of 1680 hours (57% time saved of experimental test procedure) when the thermal life of the insulating material is predicted using BRP neural network. INDEX TERMS Neural network, aging time, thermal life of insulation, accelerated lifetime test.
Abstract--An often-overlooked aspect during the development process of electrical machines, is the validity and accuracy of the machine material properties being used at the design stage. Designers usually consider the data provided by the materials supplier, which is measured on material in an unprocessed state. However, the fact that the machining processes required to produce the finished product (e.g. the stator core) can permanently vary the material properties is very often neglected. This paper therefore deals with and investigates the effects that such processes can have on the overall machine performance. To do this, three sets of material data, based on 1) the materials suppliers' data, 2) materials data based on conventional characterization methods and 3) materials data based on test samples that include the manufacturing processes, are used to develop three versions of the same baseline machine. The results of these three machines are then compared and the resulting variations of the machine's performance presented and described.The chosen baseline machine is a high performance and relatively high speed, aerospace, electrical machine. Special attention is focused on the efficiency maps of the machine as this aspect is highly dependent on the material properties that are the most sensitive to manufacturing processes such as the material's anhysteretic BH curve and its specific core loss.Index Terms--magnetic materials, manufacturing processes, performance analysis, permanent magnet motors.
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