The analysis of acoustic noise characteristics is important for effective noise reduction. This paper presents a computationally efficient approach for acoustic noise analysis of an interior permanent magnet (IPM) motor. A combination of surface velocity results calculated using ANSYS are coupled with AVL EXCITE Acoustics to estimate the air-borne noise produced by the motor. The air-borne noise is calculated using the Wave Based Technique (WBT) instead of an element-based technique such as Finite Element Analysis (FEA). The WBT requires less fine element discretization which results in smaller numerical models. The WBT mesh is generated by an automated process and merged to reduce the number of elements for optimizing the computational performance. The acoustic noise analysis for this motor includes the characterization of frequency band analysis including the narrow band, octave band, and third octave band air-borne noise calculated by the WBT-based approach. The characteristics of the generated air-borne noise for this motor are analyzed and presented. High sound pressure level (SPL) due to dominant harmonic frequencies and natural frequency excitation is explained.

This paper studies the electromagnetic interference (EMI) noise modeling for a three-level active neutral point clamped inverter. Although time-domain detailed modeling leads to more accurate results, frequency-domain modeling techniques are commonly used as a faster prediction method. Compared with the conventional differential mode (DM) EMI modeling of power inverters that use only one current source, it is shown that two current sources are required for more precise modeling. A new unified model is proposed for an active neutral point clamped inverter based on the existing equivalent circuit models for common mode (CM) EMI and the developed DM model. Both CM and DM EMI emissions are predicted by the proposed model and compared with the time-domain results. Simulation results indicate that the modeling precision is improved by 56%.

In this paper, a new method to reduce the steady state torque tracking error of finite control set model predictive torque control for switched reluctance motor drives is proposed. The steady state tracking error is considered as one of the main shortcomings of the conventional Finite Control Set Model Predictive Control (FCS-MPC). This can happen due to parameter uncertainties or when the multiple objectives are achieved by a single function with weighting factors. In the conventional model predictive torque control for SRM, the control action is obtained by a multi-objective cost function designed to track a reference torque while minimizing the phase currents over the prediction horizon. The optimal switching state which minimizes the cost function is selected and applied at each switching instant, which results in the steady state torque tracking error. In this paper, a compensation term is added to the reference torque at each sample instant to minimize the torque tracking error. The compensation term is calculated based on the estimated average torque tracking error in the previous sample times. Simulations on a three phase, 12/8, 2.3 kW SRM show promising results with the proposed method as compared to the conventional FCS-MPC.

This paper introduces a design methodology for a Switched Reluctance Motor (SRM) for an 80 kW Battery Electric Vehicle (BEV) propulsion application. The methodology aims to satisfy the high power density requirement targeted for an electric motor for a BEV application, while maintaining improved efficiency and torque quality. Iterative modeling effort has been employed for the design, including finite element analysis for the electromagnetic characteristics of the motor and dynamic modeling for performance analyses. The design approach starts with determining the motor geometry followed by sensitivity analysis for the motor performance considering various motor parameters. Then the SRM conduction angles are optimized with multi-objective genetic algorithm to improve the torque density and reduce torque ripple. The performance is further analyzed and compared to the target motor.

This paper presents an online adaptive method to adjust the phase turn-off angle in finite control set model predictive control (FCS-MPC) of switched reluctance motor (SRM) drives to reduce the negative torque production. The current in the inductance falling region generates negative torque which increases the torque ripple, RMS current, and reduces the efficiency, especially at high speed operation. The existing FCS-MPC needs long prediction horizon to predict and reduce the negative torque production by adjusting the turn-off angle. However, long prediction horizon substantially increases the computational burden. This paper proposes a simple online method to automatically adjust the phase turn-off angle for single prediction horizon FCS-MPC. The proposed method is validated in simulations for a three phase, 12/8, 2.3 kW SRM in the entire operating region. The comparison with the existing MPC shows the effectiveness of the proposed method in terms of efficiency and torque ripple reduction.

This paper presents the design of an external-rotor switched reluctance motor (SRM)for a direct-drive washing machine application and a comparison with a commercial model. First, a complete review of the state-of-the-art for “washing appliances” has been discussed and design requirements have been analyzed. Secondly, an external-rotor SRM with high number of poles has been chosen to obtain high torque density and power density. Then both, the electromagnetic and thermal performance of the external SRM have been evaluated by finite element analysis (FEA)in wash mode and spin cycle mode operations to confirm that the design fully satisfies the specifications at low and high-power range with high reliability. The mechanical prototype of the SRM has also been provided, which uses the same housing as the commercial benchmark model. Finally, the SRM design for the washing machine application has been validated based on experimental results.

In most mechanical and electrical systems, reliability is paramount. Consumers, users and operators trust that their product will perform properly over its expected lifetime. Electric Vehicles (EVs) and Hybrid-Electric Vehicles (HEVs) are no exception to this statement. One of the most important components to consider when determining the reliability of EVs and HEVs is the electric motor. Within electric motors, many things can cause reduced reliability. This paper will focus on the most important component: the bearings. In this review paper, a general summary of bearing failure methods is provided along with an in-depth analysis of all facets relating to the shaft voltage and bearing current phenomenon with specific discussion relating to EV and HEV motors. Subsequently, this paper will provide a review of several mitigation techniques to render the bearing current failure mode obsolete.

Online estimation of losses is important to improve control, operation and monitoring of electrical machines. This paper focuses on investigation of iron losses using a magnetic circuit model for its accuracy and adaptability to online estimation purposes. A customized magnetic circuit of an IPM machine is proposed that captures slotting, non linearity, cross saturation and localized effect of flux bridges. A technique is presented to compute the alternating stator and rotor flux from the static magnetic circuit. The required computations are reduced introducing pseudo mmf sources that avoid solving magnetic circuit at several rotational steps. The stator and rotor core losses are found corresponding to each harmonic component of alternating flux density. The results are validated with Finite Element analysis with good correlation.

This paper presents a comprehensive methodology for topology evaluation of bidirectional boost converters in electrified vehicle applications for higher power density. Based on the given specifications, major components in the converters are designed and sized, including the boost inductor, power module, and DC-link capacitor. 3D Finite Element Analysis (FEA) simulations are conducted to evaluate the inductor loss. Power module loss is estimated based on the manufacturer datasheet parameters. With the proposed evaluation methodology, four bidirectional boost converter topologies are compared in terms of efficiency and power density: Single Phase Single Inductor (SPSI), Dual Phase Single Inductor (DPSI), Dual Phase Dual Inductor (DPDI), and Dual Phase Coupled Inductor (DPCI). The comparison results show that the power efficiency of DPCI is highest among these topologies, and DPCI can reduce the size of the inductor and DC-link capacitor. Therefore, DPCI is a strong candidate for bidirectional boost converter in electrified vehicle applications.