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Developing a permanent magnet synchronous machine (PMSM) for direct-drive electric vehicle (EV) has challenges such as obtaining high torque density and low torque ripple. The PMSM should have high pole numbers owing to low-speed operation, thereby increasing the use of rare earth magnets and cost. Therefore, in this article a consequent pole (CP) rotor topology is proposed in which the permanent magnet (PM) volume is reduced when compared with conventional surface PMSM (SPMSM). However, replacing south poles in an SPMSM with induced steel poles can increase torque ripple and reduce torque density. In order to improve torque density in a CP PMSM, structural modifications such as multilayer windings and non-ferromagnetic barriers have been proposed in the literature. These modifications increased the torque density while increasing the torque ripple. Therefore, this article proposes a novel two-level optimization method based on gradient descent algorithm, to address the challenges of improving torque density and reducing torque ripple simultaneously in a CP PMSM. Initially, an expression for the magnet pole arc angle is derived for CP PMSM based on magnetic equivalent circuit. A two-level optimization is performed on a baseline CP PMSM to determine the optimal magnet pole arc. The torque production and torque ripples of the optimized design are validated by simulation and experimental results.

Few of the challenges with development of a single on–board motor for direct–drive electric vehicles include high torque density and low torque ripple. Therefore, in this paper, a 36–slot, 34–pole consequent pole six–phase permanent magnet synchronous machine (PMSM) has been optimized to address the aforementioned challenges for direct–drive application. Existing literature on optimization processes that rely solely on finite element models are restricted to three–phase machines only and also take longer computation time. Therefore, this paper proposes a novel optimization approach based on supervised machine learning for six–phase PMSM. In this approach, a non–conventional extended dual dq– frame model that accounts for higher order space harmonics in inductances and flux linkages has been developed and used for accurate computation of average torque and torque ripple of six–phase PMSM. Using the performance characteristics obtained from the extended dual dq –frame model for a set of initial design candidates, support vector regression algorithm is employed for supervised machine learning and increasing solutions in the design space. Furthermore, pareto front is used for selecting optimal machine models with maximum torque density and reduced torque ripple. Multi–objective trade–offs and comparison of initial and optimized designs based on average torque, torque ripple, efficiency and cost are performed.

A stator inter-turn fault occurring in one of the phases of a three-phase induction motor (IM) gives rise to high harmonics distortion in air-gap flux density, increased torque ripple, temperature rise in the stator windings, and mechanical vibrations due to varying magnetic forces and magnetic noise. The fault leads to a change in the electromagnetic field generated when compared to that during the normal motor operation. An incipient stator fault leads to variation of machine’s parameters, causing malfunction of the motor drive. Hence, it is of significant importance to detect the incipient fault before complete motor breakdown occurs. In this paper, a novel magnetic flux reference predictive method for control has been presented by using a harmonic compensation block in coordination with deep neural network (DNN) as a feedforward method to continue safe operation of motor after occurrence of incipient stator fault. This method takes into account both time and space harmonics discrepancies produced due to the fault. The proposed method has been implemented on a 7.5 hp IM using online observer of unhealthy conditions and compensated using DNN predictive methodology.

Stator slot shifting is widely implemented in fractional–slot wound permanent magnet synchronous machines for reduction of high spatial harmonic contents resulting in improved efficiency and flux–weakening performance. Existing method involves using twice the number of stator slots present thereby resulting in a winding topology with a coil pitch of 2. However, in this process, many slot–pole combinations, especially with odd slot numbers are neglected. Therefore, this paper proposes a procedure for stator slot shifting for n–phase fractional–slot wound machines considering all feasible slot–pole combinations. The slot–plot combinations are analyzed for performance attributes including winding factor, total harmonic distortion, minimum cogging torque and maximum torque per unit winding losses. Further, a non–dominated sorting genetic algorithm is used for optimal slot–pole selection for each phase number. The resultant machine performance characteristics of a sample best design candidate is verified using experimental data and finite element analysis.

Replacing half of the number of poles in a conventional surface permanent magnet synchronous machine (PMSM) reduces the use of rare earth materials delivering similar performance. However, in such a consequent pole configuration certain slot–pole numbers deliver high magnitudes of even order harmonics in the induced EMF waveforms leading to unbalanced magnetic force. Further, utilizing concentrated windings (CW) in the stator adds to this space harmonic content. In literature, slot–pole combinations were selected based on fundamental winding factor, cogging torque and net force on the machine, neglecting space harmonics content in inductance and induced EMF waveforms. Motivated by the drawbacks in the existing methods, in this paper, a novel inductance harmonics factor and EMF harmonics factor have been modelled using winding function method for a consequent pole multiphase CW PMSM. Furthermore, a gradient descent algorithm–based approach is implemented to optimally select slot–pole combination, with reduced inductance and EMF harmonics, for three–, five– and six–phase FSCW PMSM, with little prior knowledge about structural information of the machine. The inductance and induced EMF harmonics for optimal slot–pole combinations obtained from the algorithm are verified using finite element and experimental analysis.

Inverter-fed motor drive operating an unbalanced induction motor (IM) has high harmonic content which inflicts large torque ripple on the load. Dead short circuit and incipient or partial short circuit in motor stator windings lead to asymmetry in the machine parameters. Consequently, an imbalance in the voltage supply worsens the condition deteriorating the optimal performance of the drive-based motor due to the injection of both increased time and spatial harmonics. It is of primary importance that these discrepancies are taken care of while modeling a more fault tolerant, reduced harmonics drive system. This paper proposes a novel control strategy to minimize torque ripple by considering the time harmonics produced due to imbalance in inverter voltage and parameters of the faulty IM, and the estimated space harmonics from the measured magnetic flux density in a transient magnetic phenomenon. The proposed control strategy has been implemented on an unbalanced aluminum-rotor IM with online monitoring of unhealthy conditions and feeding it to the harmonic compensation block of the drive system.

Fractional slot concentrated windings (FSCW) permanent magnet synchronous machines (PMSM) have high content of space harmonics in the magnetomotive force (MMF) due to which the harmonic inductance is much larger than the magnetizing inductance [1]. These inductance harmonics lead to high torque ripple and low power factor. In case of FSCW, the coils are full pitched and cannot be chorded like in distributed windings to reduce inductance harmonics and also a suitable rotor structure have small impact on reduction of these harmonics. However, the space harmonic content in the FSCW PMSM vary significantly with the choice of slot-pole combination. Thus, the inductance harmonics can be modeled and minimized using an optimal choice of machine phases (m), stator slot numbers (S) and rotor poles (P). State of the art: [2] has presented the selection of slot, pole and phase numbers for reducing harmonic leakage inductance specifically for single layer CW PMSM. In [3], a detailed procedure for slot-pole selection based on inductances for single and double layer windings are provided. However, these are restricted for odd phase numbers and the selection process is time consuming. In this paper, the impact of winding layers, phase belt, slots, poles, and phase numbers on inductance harmonics has been studied. Further, an Adapative gradient (Adagrad) algorithm based approach is implemented to optimally select these parameters with little prior knowledge about the structural data.

Space and time harmonics in an induction motor (IM) increases due to motor fault and eccentricity leading to voltage unbalance and harmonic distortion.

Inter-turn insulation failure in induction motor results in complete or developing winding short circuit. Stator winding fault leads to an unbalance in the three phases of the motor leading to a faulty induction motor with increased time and space harmonics of flux. This can lead to uneven distribution of air gap flux and increase in torque ripple. The condition is worsened due to an unbalance in the voltage supply depreciating the optimal performance of the drive-based It is of primary importance that the aforementioned discrepancies are taken care of while modelling a more fault tolerant motor drive system with faster processing and lower response time. This paper proposes a novel control technique to reduce the unbalance in the motor due to stator fault by taking into account the air-gap flux developed in the motor and harmonics generated. An improved swarm optimization algorithm has been used in order to efficiently predict the flux reference for the stator-flux controlled motor drive. The proposed detection scheme has been implemented on an aluminum-rotor induction motor with incipient stator inter-fault with the help of online monitoring of unhealthy conditions and using it as a feedback for the drive system, thereby a robust online detection of fault and a stable fault control system.

In this paper, a novel 3-D sub-domain analytical model is developed to determine magnetic flux distribution in single-cage induction machines (IMs) with skewed rotor bars under no-load condition in an effort to more detailed analysis of spatial harmonics. The proposed model, along with an optimization algorithm, is as an alternative solution to finite-element analysis (FEA) in optimizing the geometry of IMs. The analytical method is based on the resolution of 3-D Laplace and Poisson's equations in cylindrical coordinates using the separation of variables method to calculate the magnetic vector potential for corresponding sub-domain. The proposed model includes the effect of slotting and tooth tips for the stator and rotor slots, which is usually neglected in a 2-D analysis due to the complexity of differential equations. Also, the proposed 3-D model can be used for any slot-pole combination in addition to considering the asymmetrical effect in the axial direction, which is a source of noise, vibration, and excessive losses in IMs. To evaluate the performance of the proposed 3-D analytical model, calculated magnetic-field distribution is compared with the results obtained from the 3-D FEA.

A stator winding fault in one phase of induction motor (IM) gives rise to higher harmonics distortion, increased torque ripple, temperature rise in the magnetic material, mechanical vibrations due to varying magnetic forces and magnetic noise. The fault leads to a change in the electromagnetic field generated in the motor as compared to the normal operation of motor. The copper losses generated in stator increases, thus leading to overall increase in the temperature of the motor. Looking from the aspect of electrical equivalent circuit model, the parameters of the motor changes due the occurrence of fault, which makes it difficult for designing a drive for the motor. In this paper a novel computational model has been presented which uses both artificial neural network model (ANN) and lumped parameter thermal network (LPTN) for parameter estimation and calculation of losses which can be used for designing a fault-tolerant, loss minimizing drive. This dual network model has been and experimented on a 7.5 hp aluminum-rotor induction motor.

Due to the advantages of higher efficiency, low manufacturing cost and lower machine weight, Copper Rotor Induction Motor (CRIM) is a suitable cost effective alternative choice over permanent magnet motor in EV/HEV traction applications [1]. However, temperature rise issue is a critical factor that has direct effects on machine parameters such as effective resistances and inductances as well as magnetic properties of the machine materials. In this paper, a lumped parameter thermal network (LPTN) model is proposed to predict transient thermal behaviour in a Totally Enclosed Fan Cooled (TEFC) CRIM considering non-existent of forced convection heat transfer in stator end-winding due to smooth rotor geometry. The model also takes into consideration of various losses as heat sources that are determined from motor loading experiments. In order to validate thermal model, a 20-hp CRIM is tested under varying speed and loading conditions to measure the actual operating temperature rise and compared with calculated temperature rise.

The percentage of copper losses in the induction machine (IM) governs the performance and hence the efficiency of the machine. The temperature dependence of stator resistance results in inaccurate performance prediction if temperature is not taken into account in advance. Hence, it is imperative to determine the relationship between temperature and stator resistance. This research paper investigates the change in stator resistance with increase in temperature over a period of time. An improved particle swarm optimization based scheme is developed for the estimation of stator resistance with the help of the two-axis model of induction machine, taking into account the temperature effect on the resistance. Further, a 15 kW copper-rotor IM has been tested using a PLC based drive system and the increase in temperature is measured using resistance temperature detectors to obtain online stator resistance. A comparative analysis of the experimentally measured stator resistance with that obtained from the numerical investigation of the temperature based two-axis model has been performed.