Tanvir Rahman

A Computationally Efficient Algorithm for Rotor Design Optimization of Synchronous Reluctance Machines

A generalizable algorithm is proposed for the design optimization of synchronous reluctance machine rotors. Single-barrier models are considered to reduce the algorithms computational complexity and provide a relative comparison for rotors with different slots-per-pole combinations. Two objective values per sampled design (average and ripple torques) are computed using 2-D finite-element analysis simulations. Non-linear regression or surrogate models are trained for the two objectives through a Bayesian regularization backpropagation neural network. A multi-objective genetic algorithm is used to find the validated Pareto front solutions. An analytical ellipse constraint is then suggested to encapsulate optimal solutions. Compared with a direct sampling approach, this restriction captures an optimal region within the double-barrier space for further torque ripple reduction.

Design and optimization of fractional slot concentrated winding permanent magnet machines for class IV electric vehicles

The design, optimization and application of a number of surface mounted fractional slot concentrated winding (FSCW) electric machines for application to Class IV electric vehicles have been considered. Four FSCW motors with nominal power ratings of 50, 65, 75 and 100 kW have been designed. The motors were optimized using a novel multi-objective optimization strategy which allows a large numbers of objectives to be considered while ensuring computational efficiency and Pareto optimality. Vehicle simulations were carried out using the optimized motors for some typical drive cycles. The gear ratio of the drive train was optimized for each motor with respect to the drive cycle and the vehicle performances were calculated. The methodology and results presented provide a novel and improved framework for considering the trade-offs between the motor size, gear ratio and vehicle performance for Class IV and other vehicle classes.

Comparison of fractional-slot concentrated winding and PM-assisted synchronous reluctance motors for class IV electric vehicles

The design and comparative analysis of three 100 kW motors, a fractional-slot concentrated winding machine (FSCW) surface mounted permanent magnet and two permanent magnet-assisted synchronous reluctance machines (PMa SynRMs), were considered for a Class IV step van electric vehicle application. The electromagnetic, thermal, demagnetization, and acoustic performances of each machine have been presented as well as vehicle dynamic simulations for various urban drive cycles. The results show that all three motors are suitable for a Class IV electric vehicle. The advantages and the disadvantages of the machines with respect to their application for Class IV vehicles have been highlighted in this work.

Battery voltage optimization of a variable DC bus voltage control powertrain for medium duty delivery trucks for various drive cycles

One of the most commonly used electric drive topologies for electric vehicles is that of a permanent magnet motor powered by a two-level inverter and a high voltage battery (system S1). An alternative to this topology is to replace the high voltage battery with a low voltage battery and a DC-DC boost converter (system S2). Previous work has shown that such a design is beneficial for vehicles that usually follow daily drive cycles with low average speed and many start and stop cycles. One category of vehicles that meet these criteria are medium duty delivery trucks. It has been demonstrated that the target modulation index of system S2 can be optimized in order to minimize the energy consumption over a specific drive cycle. This paper focuses on the effect of the battery voltage on system efficiency and demonstrates that the battery voltage can be used as an optimization parameter along with the previously studied target modulation index.

Surrogate-based MOEA/D for electric motor design with scarce function evaluations

This paper proposes a surrogate-assisted multiobjective evolutionary algorithm based on decomposition (sMOEA/D) for the design of electric motors. The idea is to improve the surrogate gradually during the optimization. Simulation results show that the proposed method is competitive with state-of-the-art multiobjective optimization algorithms needing only a small number of function evaluations.

Surrogate-Based MOEA/D for Electric Motor Design With Scarce Function Evaluations

This paper proposes a novel surrogate assisted multi-objective evolutionary algorithm based on decomposition (s-MOEA/D) for the design of electric motors. The idea is to improve the surrogate gradually during the optimization. Simulation results show that the proposed method can outperform a regular multi-objective optimization algorithm with only a small portion of the computational cost.

Analysis and design of electrical machines with material uncertainties in iron and permanent magnet

Purpose Manufacturing processes, such as laminations, may introduce uncertainties in the magnetic properties of materials used in electrical machines. This issue, together with magnetization errors, can cause serious deterioration in the performance of the machines. Hence, stochastic material models are required for the study of the influences of the material uncertainties. The purpose of this paper is to present a methodology to study the impact of magnetization pattern uncertainties in permanent magnet electric machines. Design/methodology/approach The impacts of material uncertainties on the performances of an interior permanent magnet (IPM) machine were analyzed using two different robustness metrics (worst-case analysis and statistical study). In addition, two different robust design formulations were applied to robust multi-objective machine design problems. Findings The computational analyses show that material uncertainties may result in deviations of the machine performances and cause nominal solutions to become non-robust. Originality/value In this paper, the authors present stochastic models for the quantification of uncertainties in both ferromagnetic and permanent magnet materials. A robust multi-objective evolutionary algorithm is demonstrated and successfully applied to the robust design optimization of an IPM machine considering manufacturing errors and operational condition changes.

A neural network based surrogate model for predicting noise in synchronous reluctance motors

This paper proposes a neural network (NN) based noise prediction model for electric machines, applied to the case of synchronous reluctance motors (SynRMs). The natural frequencies of various vibration modes for a SynRM stator with generalized tooth geometry and slot numbers have been obtained using structural FEA based computations and then used to build a NN based surrogate model. The accuracy of the surrogate model has been tested and applied to predict the noise level in SynRMs. Also, varying trends in the noise levels for single-barrier SynRMs have been analyzed as a function of the rotors flux carrier and barrier widths using the natural frequency prediction model.

Multiple Operating Points Based Optimization: Application to Fractional Slot Concentrated Winding Electric Motors

A general strategy for the multiobjective optimization of electric machines with respect to multiple operating conditions is proposed and applied to two 10-pole 12-slot fractional slot concentrated winding (FSCW) machines. To define an optimization problem, including the effects of multiple operating points, both sensitivity analysis and conflict analysis of the design objectives were incorporated into the proposed strategy. An objective-reduction algorithm was applied in order to make the optimization process affordable under a limited computational budget. The effects of incorporating multiple operating points on the optimization of 10-pole 12-slot FSCWs are presented. The proposed methodology can be applied to solve motor optimization problems with a high number of objectives, in general.

Restricting the design space of multiple-barrier rotors of synchronous reluctance machines

Purpose This paper aims to propose a numerical methodology to reduce the number of computations required to optimally design the rotors of synchronous reluctance machines (SynRMs) with multiple barriers. Design/methodology/approach Two objectives, average torque and torque ripple, have been simulated for thousands of SynRM models using 2D finite element analysis. Different rotor topologies (i.e. number of flux barriers) were statistically analyzed to find their respective design correlation for high average torque solutions. From this information, optimal geometrical constraints were then found to restrict the design space of multiple-barrier rotors. Findings Statistical analysis of two considered SynRM case studies demonstrated a design similarity between the different number of flux barriers. Upon setting the optimal geometrical constraints, it was observed that the design space of multiple-barrier rotors reduced by more than 56 per cent for both models. Originality/value Using the proposed methodology, optimal geometrical constraints of a multiple-barrier SynRM rotor can be found to restrict its corresponding design space. This approach can handle the curse of dimensionality when the number of geometric parameters increases. Also, it can potentially reduce the number of initial samples required prior to a multi-objective optimization.