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Optimization Techniques for Cogging Torque Reduction and Thermal Characterization in Brushless DC Motor
Abstract
This paper presents soft computing-based optimization techniques for the cogging torque reduction and thermal characterization by finite element analysis in a permanent magnet brushless DC motor (BLDC). Stator and rotor structure of BLDC motor are optimized to reduce the cogging torque, noise, and vibration by using the design parameters namely: length of magnet, length of air gap and opening in the stator slot which are selected by performing variance-based sensitivity analysis. The proposed method is suitable in the preliminary design phase of the motor to determine the optimal structure to improve the efficiency. The comparison of results obtained using firefly algorithm , ant colony optimization algorithm and Bat algorithm indicate that Firefly-based optimization algorithm is capable of giving improved design parameter output. Cogging torque is created due to the interaction of magnets in the rotor and the stator slot of the motor. Thorough thermal analysis is also conceded out to investigate the thermal characteristics at dissimilar portions of the motor namely: stator core, stator slot, rotor core and permanent magnet at different operating environments in the continuous operation mode. Thermal investigation is required for the various high speed e-vehicle applications. The usefulness of the designed machine simulation is compared with the results obtained from hardware analysis. The outcomes attained from software simulation studies are validated through experimental hardware setup.
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In this article, the design of brushless DC (BLDC) motor is performed using multiobjective optimization algorithm (MOOA) by satisfying multiple objectives. Initially, sensitivity analysis is carried out to find the most influencing parameters that affect the performance of BLDC motor. MOOAs such as Pareto envelope‐based selection algorithm (PESA), Pareto archived evolution strategy (PAES) and nondominated sorting genetic algorithm‐II (NSGA‐II) is employed in the optimal design of the BLDC motor. The proposed MOOA have three objectives namely: output torque maximization, volume minimization, and minimization of total losses. MOOAs are analyzed using performance metrics and qualitative comparison is provided to select the best algorithm. Later, finite element method (FEM) is used to investigate the transient and thermal characterization on the BLDC motor designed using NSGA‐II. The thermal results thus obtained using NSGA‐II for the above motor under different operating conditions is also compared with the existing single objective optimization algorithm. From the comparisons, it is observed that NSGA‐II algorithm significantly outperforms the existing single objective optimization algorithm. Finally, the usefulness of the designed machine based on NSGA‐II is compared with the results obtained from simulation and hardware analysis. In this article, design of brushless DC (BLDC) motor has been optimized using multi objective optimization (MOO) algorithms. The thermal analysis and transient analysis has been carried out on the BLDC designed used nondominated sorted genetic algorithm‐II (NSGA‐II).The usefulness of the designed machine based on NSGA‐II is compared with the results obtained from simulation and hardware analysis.
The design of a good PID controller for speed control of Brushless dc (BLDC) motor is essential for its successful operation. This paper presents a design and implementation of recently developed nature inspired Flower Pollination Algorithm for speed control of BLDC motor with optimal PID tuning. In the present work, optimization based approach is applied for tuning of PID speed controller by considering an integral square error as the objective function. The effectiveness of the presented work is compared with the conventional Ziegler-Nichols method and other existing nature inspired optimization methods such as PSO and Firefly algorithms, and the same is validated through real-time implementation with dSPACE DS1103 controller board.
In this paper, Multi-Objective Optimization (MOO) techniques namely: Weighted Sum Method (WSM), Multi Objective Genetic Algorithm (MOGA) and Niched Pareto Genetic Algorithm (NPGA) are proposed for the design optimization and analysis of magnetic flux distribution for a slotless permanent magnet Brushless DC (BLDC) motor. The sensitivity analysis is carried out to identify the design variables of BLDC motor which influence the objective function. The objective functions are conflicting in nature (maximization of output torque and minimization of total volume and losses), hence, are optimized simultaneously by means of MOO algorithms. The proposed MOOs account for four kinds of design variables namely: rotor radius, stator/ rotor axial length, magnet thickness and winding thickness simultaneously to maximize the output torque and to reduce the total volume and total power loss. The conventional MOO such as WSM gives single pareto optimal solution. However, the proposed MOGA and NPGA algorithms create accurate and well-distributed pareto front set with few function evaluations. The performances of the three MOOs are evaluated and compared using the four performance metrics namely: Hyper Volume (HV), Generational Distance (GD), Inverted Generational Distance (IGD) and Spread. From the comparison, it is observed that NPGA gives better results. Using the design parameter obtained from NPGA, the magnetic flux distribution analysis of the BLDC motor is carried out to analyze variation of flux distribution in the different parts of the motor. The results thus obtained are compared with those obtained through Partial differential equation and single objective GA optimization. The detailed thermal analysis is also carried out to analyze the thermal behavior at different parts of the machine for different working conditions in the continuous operation mode and hence, the results obtained are compared with single objective GA optimization. The advantages of MOOs in the design optimization of slotless permanent magnet BLDC motor are highlighted.
This paper proposes a generalized regression neural network (GRNN) based algorithm for data interpolation and design optimization of brushless dc (BLDC) motor. The procedure makes use of magnet length, stator slot opening and air gap length as design variables. Cogging torque and average torque are treated as performance indices. The optimal design necessitates mitigating the cogging torque and maximizing the average torque by varying design variables. The data set for interpolation and ensuing design optimisation using GRNN is obtained by modeling a standard BLDC motor using finite element analysis (FEA) tool MagNet 7.1.1. The performance indices of the standard motor obtained using FEA are validated with an experimental model and an analytical method. The optimal design is authenticated using particle swarm optimization (PSO) algorithm and the performance indices of the optimal design obtained using GRNN is validated using FEA. The results indicate the suitability of GRNN as an interpolation and design optimization tool for a BLDC motor.
This paper presents a metaheuristic algorithm inspired in evolutionary computation and swarm intelligence concepts and fundamentals of echolocation of micro bats. The aim is to optimize the mono and multiobjective optimization problems related to the brushless DC wheel motor problems, which has 5 design parameters and 6 constraints for the mono-objective problem and 2 objectives, 5 design parameters, and 5 constraints for multiobjective version. Furthermore, results are compared with other optimization approaches proposed in the recent literature, showing the feasibility of this newly introduced technique to high nonlinear problems in electromagnetics.
The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. Particle swarm optimization (PSO) is one of the modern metaheuristics of swarm intelligence, which can be effectively used to solve nonlinear and non-continuous optimization problems. The basic principle of PSO algorithm is formed on the assumption that potential solutions (particles) will be flown through hyperspace with acceleration towards more optimum solutions. Each particle adjusts its flying according to the flying experiences of both itself and its companions using equations of position and velocity. During the process, the coordinates in hyperspace associated with its previous best fitness solution and the overall best value attained so far by other particles within the group are kept track and recorded in the memory. In recent years, PSO approaches have been successfully implemented to different problem domains with multiple objectives. In this paper, a multiobjective PSO approach, based on concepts of Pareto optimality, dominance, archiving external with elite particles and truncated Cauchy distribution, is proposed and applied in the design with the constraints presence of a brushless DC (Direct Current) wheel motor. Promising results in terms of convergence and spacing performance metrics indicate that the proposed multiobjective PSO scheme is capable of producing good solutions.
In the theory of inverse problem, the parameters identification by optimization is considered as one of its main applications. This paper presents an optimal design of a slotted permanent magnet Brushless DC (BLDC) motor with surface mounted magnets. The inverse problem method is applied by using a thriving solver afforded by the nonlinear optimization toolbox of Matlab ‘Fmincon’, this function is based on Active-Set and Sequential Quadratic Programming approaches with calculation of the Hessian from Quasi-Newton algorithm. The optimal magnetic field density considered as the main objective is obtained by picking several parameters and analyzing their effects. The proposed approach is highlighted by using the obtained parameters in the design of the motor. The Finite element method is applied on the motor for numerical analysis by using FEMM magnetic coupled with Matlab code. Effectiveness and robustness of the proposed approach are verified by a comparison between the initial and optimized design.
The aim of this work is to design speed and
current controllers of a brushless dc (BLDC) motor to drive a
hybrid electric-bike. The system is fed from two hybrid sources
for driving the motor and charging of storage elements; one is a
photovoltaic (PV) generator as a green and neat source; and the
other is a human-powered pedal dc-generator. The proposed
design of the controllers is formulated as an optimization
problem to overcome the most static and dynamic fluctuations of
the system. The ant-colony optimization (ACO) algorithm is
employed to search for the optimal proportional- integralderivative
(PID) parameters of the proposed controllers by
minimizing the time domain of the objective function. The
performance of the system is analysed when using the proposed
controller with and without storage elements. Extensive
simulation results are provided to validate the effectiveness and
robustness of the proposed approach against system dynamics
and PV-fluctuations. The obtained results confirm the better
performance of the system with the proposed controllers for
several speed trajectories of the drive compared to the classical
PID-controllers.
Electric energy is the most popular form of energy because it can be transported easily at high efficiency and reasonable cost. Nowadays the real-world electric power systems are large-scale and highly complex interconnected transmission systems. The transmission expansion planning (TEP) problem is a large-scale optimization, complicated and nonlinear problem that the number of candidate solutions increases exponentially with system size. Investment cost, reliability (both adequacy and security), and congestion cost are considered in this optimization. To overcome the difficulties in solving the non-convex and mixed integer nature of this optimization problem, this paper offers a firefly algorithm (FA) to solve this problem. In this paper it is shown that FA, like other heuristic optimization algorithms, can solve the problem in a better manner compare with other methods such genetic algorithm (GA), particle swarm optimization (PSO), Simulated Annealing (SA) and Differential Evolution (DE). To show the feasibility of proposed method, applied model has been considered in IEEE 24-Bus, IEEE 118-Bus and Iran 400-KV transmission grid case studies for TEP problem in both adequacy and security modes. The obtained results show the capability of the proposed method. A comprehensive analysis of the GA, PSO, SA and DE with proposed method is also presented.
This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using an improved bee algorithm (IBA). The characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. This method is based on the capability of swarm-based algorithms in finding the optimal solution. One sample case is used to illustrate the performance of the design approach and optimization technique. The IBA has a better performance and speed of convergence compared with bee algorithm (BA). Simulation results show that the proposed method has a very high/efficient performance.
Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Permanent-magnet synchronous machines can be designed to obtain high efficiency and high torque density. Population-based optimization methods such as genetic algorithms and particle swarm optimization are gaining acceptance as a means of optimizing the design of this class of machines. This paper builds on the literature by utilizing a computationally efficient machine analysis appropriate for use with population-based optimization methods that enables the consideration of a significantly larger search space than previously reported in the literature. It is also unique in that the relative performance of different parameter encoding and objective function formulations are considered.
The influence of various design parameters on the cogging torque
developed by permanent magnet machines is investigated. It is shown that
the slot and pole number combination has a significant effect on the
cogging torque, and influences the optimal value of both skew angle and
magnet arc, as well as determining the optimal number of auxiliary
teeth/slots. A simple factor, which is proportional to the slot number
and the pole number and inversely proportional to their smallest common
multiple, has been introduced to indicate the
“goodness”β of the slot and pole number combination. In
general, the higher the “goodness” factor the larger the
cogging torque
Research has indicated that the permanent magnet motor drives,
which include the permanent magnet synchronous motor (PMSM) and the
brushless DC motor (BDCM) could become serious competitors to the
induction motor for servo applications. The PMSM has a sinusoidal back
EMF and requires sinusoidal stator currents to produce constant torque
while the BDCM has a trapezoidal back EMF and requires rectangular
stator currents to produce constant torque. The PMSM is very similar to
the wound rotor synchronous machine except that the PMSM that is used
for servo applications tends not to have any damper windings and
excitation is provided by a permanent magnet instead of a field winding.
Hence the d , q model of the PMSM can be derived from
the well-known model of the synchronous machine with the equations of
the damper windings and field current dynamics removed. Because of the
nonsinusoidal variation of the mutual inductances between the stator and
rotor in the BDCM, it is also shown that no particular advantage exists
in transforming the abc equations of the BCDM to the
d , q frame. Hence the solution of the original abc
equations is proposed for the BDCM
The permanent magnet synchronous motor (PMSM) and the brushless DC
motor (BDCM) have many similarities; they both have permanent magnets on
the rotor and require alternating stator currents to produce constant
torque. For application considerations, these two motor drives have to
be differentiated on the basis of known engineering criteria. Some of
the criteria used to assess these two machines include power density,
torque per unit current, speed range, feedback devices, inverter rating,
cogging torque, ripple torque, and parameter sensitivity. Guidelines for
the appropriate machine to be used for a given application are given
based on the results of the criteria
