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In this study, a high-speed switched reluctance machine (HS-SRM) with cobalt-iron lamination material is proposed for spindle motors, which are used in computer numerical control machines. Wide torque-speed range, high power density, high efficiency, and low cost are the crucial issues in spindle applications. Three types of electric machine candidates, the permanent magnet synchronous machine (PMSM), induction machine (IM), and switched reluctance machine (SRM), are compared with their outstanding features. The SRM spindle motor will offer a more robust, reliable, compact, and cost-effective solution compared to the IM or PMSM spindle applications. Moreover, advancement on cobalt-iron laminations gives a chance to the SRM for competing with the PMSM and IM in terms of power density and efficiency. In this study, a high-speed SRM is designed, optimized, and analyzed. Its performance metrics are obtained based on torque-speed range and efficiency over a wide speed range. The proposed SRM is compared with existing industrial products in terms of power densities. Then the design is verified via experimental study. The results show that the HS-SRM with cobalt-iron lamination material offers ultimate power density and efficiency in wide operating conditions. [ABSTRACT FROM AUTHOR]

In this study, an analytical model is proposed for natural frequency calculation and acoustic noise prediction for high speed switched reluctance machines. The developed natural frequency model results are compared with the mechanical finite element analysis results in terms of 6 different mode shapes that cause the majority of the acoustic noise in switched reluctance machines. The results show that the analytical results are consistent with the numerical method results with minimum 90% matching. Based on the natural frequency calculation model, a new acoustic noise prediction method is developed that only needs a radial force waveform as an input emerging on stator pole surfaces. The comparison of the developed and the numerical results clearly indicates that the acoustic noise level of the switched reluctance machine can be effectively found during the design process without using time-consuming numerical methods. [ABSTRACT FROM AUTHOR]

The aim of this paper is to optimize an 8/6 doubly salient switched reluctance machine using three computational intelligence methods, which include particle swarm optimization, a genetic algorithm, and differential evolution. Three cases are investigated where different parameters are considered like the stator pole arc, rotor pole arc, and ratios, which define the stator yoke and rotor thickness. The objective functions considered are the average torque and the torque-to-weight functions. The simulations are carried out using MATLAB and FEMM software. The optimal results found are compared with the initial design, and it is shown that high improvements are achieved. [ABSTRACT FROM AUTHOR]

The electric drive system is one of the key technologies for electric vehicles. First, the energy ow of an electric vehicle is analyzed in the driving, braking, and charging working states. After that, a drive topology that can achieve 3 working states is proposed based on the driving characteristics of a switched reluctance machine. In the driving state, the boost mode of a front-end converter is used to achieve different operating conditions, including low speed with a light load or high speed with a heavy load. In the braking state, the front-end converter is used to implement smooth switching between initial excitation and braking energy feedback. In the charging state, a new power factor correction circuit is proposed, and the motor windings and power converter constitute the onboard charger, whose power factor is correctable. According to the proposed drive topology, the control strategies for 3 working states are put forward. Finally, an experimental platform is built to verify the correctness of the control strategies as well as the exibility of the whole system. [ABSTRACT FROM AUTHOR]