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The purpose of this paper is to suggest a novel current sensor-less drive system for a novel axial flux-switching permanent-magnet motor drive to reduce the costs and avoid problems caused by faults of the current sensors.

Commonly, a conventional controller needs at least two current sensors; in this paper, the current sensors are removed by replacing estimated stator current with the extended Kalman filter.

A prototype of the novel axial flux-switching permanent-magnet motor is fabricated and tested. It is found that the experimental results confirm the proposed method and show that the control has almost the same performance and ability as the conventional control.

The axial flux-switching permanent-magnet motor is one of the most efficient motors, but current sensor-less control of an axial flux-switching permanent-magnet motor with a sandwiched permanent magnet and a unity displacement winding factor has not been specially reported to date. Thus, in this paper, the authors report on current sensor-less control based on the extended Kalman filter for electric vehicles.

In this paper, an improved multiobjective particle swarm optimization (PSO) algorithm is proposed to optimize a three-phase, 12-slot, 19-pole, yokeless axial-field flux-switching permanent magnet (YASA-AFFSPM) motor.

Based on the structural characteristics of the YASA-AFFSPM, a mathematical model is established to calculate the main size of the YASA-AFFSPM motor. The split ratio, stator axial length, sandwiching pole angle, rotor pole angle, PM arc and number of conductors per slot are selected as optimization variables. Also, the efficiency, power factor, cogging torque and average torque are considered as the optimization objectives. The objectives are optimized by combining the improved multiobjective PSO algorithm with electromagnetic calculation.

Based on the proposed algorithm, the investigated motor is optimized. The on-load efficiency, power factor and average torque of the motor performance have increased by 0.87%, 3.13% and 10.39%, respectively. Moreover, the cogging torque and slot fill factor have undergone decreases of 8.57% and 3.34%, respectively. Finally, the effectiveness of the algorithm is verified using experiment results.

So far, no comprehensive report has been observed on the optimization of the YASA-AFFSPM motor using evolutionary algorithms and the study of the effect of the motor parameters. Therefore, in this paper, the authors decided to investigate the effect of YASA-AFFSPM motor parameters and improve motor performance with the improved PSO method.

The purpose of this paper is to investigate a novel axial flux-switching motor with sandwiched permanent magnet for direct drive electric vehicles (EVs), in which the torque density is increased and the cogging torque is decreased. For reducing the back-electromotive force (EMF) harmonics and cogging torque, a twisted structure is employed. To improve the dynamic performance of the axial field flux-switching sandwiched permanent magnet (AFFSSPM) motor a space vector modulation-direct torque and flux control scheme is proposed.

A multi-objective optimization is performed by means of artificial neural network and non-sorting genetic algorithm II to minimize the cogging torque while preserving the average torque.

A comparative study between two proposed machines and the conventional flux-switching permanent magnet (FSPM) machine is accomplished and the static electromagnetic characteristics are analyzed. It is demonstrated that the proposed model with twisted structure has significantly improved performance over the conventional FSPM machine in back-EMF and efficiency. The proposed controller has a speed loop only and contains neither the current loop nor hysteresis control. The AFFSSPM motor exhibits excellent dynamic performance with this scheme.

The axial flux-switching permanent-magnet machine is one of the most efficient machines but the AFFSSPM with sandwiched permanent magnet has not been specially reported to date. Thus in this paper, the authors report on optimal design of an axial flux-switching sandwiched permanent magnet machine for electric vehicles and investigate its dynamic performance.

The purpose of this paper is to propose a novel axial field flux-switching machine with sandwiched permanent magnets. It is one of the most efficient machines which is appropriate for high-torque and low-speed direct-drive applications. The proposed model is equipped with an advanced phase-group concentrated-coil winding to obtain a unity displacement winding factor. Two configurations of the proposed motors with 6-stator-slot (S)/10-rotor-pole (P) and 12S/19P are investigated. These two structures are compared with optimized a conventional axial-field flux-switching permanent-magnet (CAFFSPM) machine. Unity displacement winding factor increases the back-EMF and electromagnetic torque. Moreover, the prototype 12S/19P motor is built to verify the design.

The torque equation is obtained and the dimensions of the two proposed motors are determined. Some specific design issues, including the stator/rotor pole sandwiching pole angle, the stator tooth angle and the rotor pole angle have been optimized to minimize the cogging torque while maintaining the high torque density by means of response surface methodology (RSM) and 3-D finite element model of the machine.

To improve the performance, especially at high torque density, low cogging torque and high level of fault-tolerant capability, the 12S/19P axial field flux-switching sandwiched permanent-magnet (AFFSSPM) motor is proposed. Based on the optimized design, a prototype of the 12S/19P AFFSSPM motor is fabricated and tested. It is found that the experimental results validate the 3-D finite element method (FEM) simulation results.

The AFFSSPM motor is one of the most efficient motors, but the 12S/19P AFFSSPM motor with sandwiched permanent magnet and unity displacement winding factor has not been specially reported to date. Thus, in this paper, the authors report on optimal design of a novel axial flux-switching sandwiched permanent-magnet machine for electric vehicles and fabricate a prototype of the 12S/19P AFFSSPM motor.

The purpose of this paper is to present novel search formulations in gradient‐type methods for prediction of boundary heat flux distribution in two‐dimensional nonlinear heat conduction problems.

The performance of gradient‐type methods is strongly contingent upon the effective determination of the search direction. Based on the definition of this parameter, gradient‐based methods such as steepest descent, various versions of both conjugate gradient and quasi‐Newton can be distinguished. By introducing new search techniques, several examples in the presence of noise in data are studied and discussed to verify the accuracy and efficiency of the present strategies.

The verification of the proposed methods for recovering time and space varying heat flux. The performance of the proposed methods via comparisons with the classical methods involved in its derivation.

The innovation of the present method is to use a hybridization of a conjugate gradient and a quasi‐Newton method to determine the search directions in gradient‐based approaches.