Design Optimization of Axial Flux Permanent Magnet Brushless DC Micromotor Using Response Surface Methodology and Bat Algorithm

Major car manufacturers such as Toyota, Honda, Ford and Hyundai are engaged in serious research, development and manufacture of hybrid electric vehicles in order to promote fuel efficient and environmentally friendly cars. Electric motor is one of the major energy consuming parts in a hybrid electric vehicle. Other than the requirement to should have high efficiency irrespective of driving conditions, the electric motor must also have high torque density and compact design. This paper presents the design of an electric motor for the direct drive of electric vehicle. A permanent magnet motor was designed in the form of an axial flux inner stator-non slot type. Preliminary design for a motor with 16 rotor poles was chosen in order to achieve high torque density and stable rotation at low speed. The motor design was simulated by using Ansoft Maxwell3D electromagnetic simulation finite element method (FEM) software to get certain parameters. The motor was fabricated and constructed on a test-bed. Data from the experimental tests were obtained and the results were compared to the simulation results.

Compact motor with high torque and wide speed range is the primary requirement for in-wheel electric vehicle application. Axial flux permanent magnet (AFPM) brushless direct current (BLDC) motors are an interesting solution for electric vehicles, because of their shape, flexibility, compactness, robustness, high efficiency, wide speed range and high torque. In this paper, a slotless AFPM BLDC motor is proposed. It has single side configuration with 24 stator poles and 36 permanent magnets (PM) on rotor disc. In most of the applications of AFPM BLDC motor, the speed control is very important. To achieve accurate speed control, closed loop control using microcontroller is proposed in this paper. Microcontroller is used to drive the motor by sensing rotor position using Hall sensors. The effective braking scheme is also proposed. To verify the performance of the proposed AFPM BLDC motor, simulation is carried out and results are presented. A prototype of AFPM BLDC motor is designed and fabricated in order to validate the performance of the motor.

Genetic algorithm (GA)-based multi-objective optimal design procedure of radial flux permanent magnet brushless DC (PMBLDC) motor is presented in this paper. Three objective functions are considered, i.e., efficiency, weight, and combination of both. The first two fitness functions are single-objective, and the third one is multi-objective. Multi-objective function is combinational function which incorporates both efficiency and weight of the motor into single fitness function. Design of motor is optimized using these three functions separately. Average flux density (B g), torque to rotor volume ratio (K trv), air gap length (l g), motor aspect ratio (A r), and motor split ratio (S r) are design variables to optimize. To validate optimized design obtained from the algorithm, finite element analysis is carried out.

This paper presents a Finite Element Method (FEM) based design and analysis of radial flux Permanent Magnet Brushless DC (PMBLDC) motor. The objective of this paper is to develop high power density and high efficiency PMBLDC motor for electric scooter application. The motor considered for FEM design is a 3-phase, 8 pole rectangular wave, which replaces the conventional Internal Combustion Engines (ICEs). The distinct features of this motor design are: (1) The magnetic yoke reduction, which will decrease the weight and size, (2) Coil span equal to one slot pitch which will result in saving of copper and (3) elimination of cogging torque because of fractional number of slots per pole. The outer rotor configuration of PMBLDC motor has been considered in this design. The spokes of the wheel is directly fitted into the outer rotor of the motor. The simulation has been carried out by Pre-flux software. The electromagnetic field analysis is used for the design and optimization of the motor which is based on FEM.

The aim of this paper is to develop a methodology to design an axial flux permanent magnet brushless direct current (AFPM) motor for a washing machine and compare the results with a conventional radial flux permanent magnet brushless direct current (RFPM) motor with the same power ratings. The AFPM motor is designed based on the maximum power density for an optimum inner-to-outer diameter ratio by using the reference RFPM motor constraints, such as the same rated torque at the rated speed. Both motors use the same materials in terms of lamination, permanent magnet, and conductor. They both use ferrite magnets; however, the AFPM motor has a surface-mounted structure and the RFPM motor has a buried one. The algorithm is used to determine the motor dimensions such as motor diameters, lengths, and the number of turns. The optimum number of poles considered for the design is chosen. Both motors are analyzed by finite element method (FEM) to verify the analytical approach and are compared in terms of power density, torque density, total weight and volume, torque ripple, and efficiency.

In this paper, a new outer-rotor permanent magnet (PM) brushless DC drive is designed and analyzed. To enable this drive to be applied to electric vehicles, its transient performances at both normal and flux-weakening operations are particularly focused. The distinct feature in design is due to the new motor configuration including the outer-rotor topology, the multipole magnetic circuit and the full slot-pitch coil span arrangement. The distinct feature in analysis is due to the development of the circuit-field-torque coupled time-stepping finite-element method. The proposed PM brushless DC drive is prototyped. The analysis results are verified by experimental measurement.

This paper reviews advances in the state of the art of applying three-dimensional finite-element (3-D-FE) magnetic field computation techniques to the analysis and quantification of parameters and performance of permanent magnet (PM) brushless DC motors as components of brushless DC drives. The application of the powerful 3-D-FE formulation based on the coupled magnetic vector potential-magnetic scalar potential (CMVP-MSP) concept to a case study PM brushless DC motor with skewed permanent magnet mounts is reviewed and summarized. This large-scale simulation of such a motor drive system is shown to be successfully implementable using workstation type computer environments.

This paper deals with a sensitivity study of an outer rotor permanent magnet (PM) brushless DC (BLDC) motor design, concerning the effect of several parameters and factors on its corresponding performance. For the purposes of this study, several design features were considered regarding the type and size of the permanent magnets and the slot geometry. Moreover, the pole-arc to pole-pitch ratio variation was investigated. The primarily aim is to draw useful conclusions for the motor overall performance according to preset requirements, such as the rated speed, motor efficiency, output power, cogging torque, overall motor weight etc. The obtained results were validated through simulations using finite element method (FEM) analysis with the aid of commercial software. The concluded remarks reveal important considerations which have to be taken into account by the designer prior and through the design phase of a BLDC motor.

High speed permanent magnet (PM) brushless dc (BLDC) motors have increasingly been used in a wide range of commercial and industrial applications, while low rotor loss and reliable rotor dynamic feature of the motor are essential requirements. Due to the high power densities, the high speed PM BLDC motors' unit volume losses are high, and also it is difficult to disperse heat, thus, the rotor losses are no longer negligible compared to the conventional-speed machines and must be considered in the design process. Besides, the rotors of high speed PM BLDC motors are subject to the resonance vibration, limiting the maximum operation speed. This paper describes the design and analysis of a high speed PM BLDC motor with surface mounted magnets for a rated power of 10kW at 70,000 rpm. Influences of the stator and rotor structures as well as magnet materials on the motor's electromagnetic performance and rotor dynamic property are analyzed. Both analytical and FEA methods are adopted to calculate the motor losses and rotor resonance modals with different motor configurations.

The three‐dimensional (3D) printed poly lactic acid (PLA) bone plates lack mechanical strength, resulting in premature failure. Coating these plates with polydopamine (PDM) forms covalent bonds with the PLA molecular structure, enhancing their mechanical properties. The mechanical strength of the coated bone plates is influenced by infill density, submersion time, shaker speed, and coating solution concentration. However, conducting experiments for each parameter value to achieve maximum biomechanical tensile strength (BTS) and biomechanical flexural strength (BFS) is time‐consuming and costly. Overall, the combination of response surface methodology (RSM) and machine learning (ML) enables determination of the best printing parameters, leading to reduced material waste, personalized bone plates tailored to individual anatomy, improved implant fit, and functionality. Moreover, this approach has the potential to reduce the need for additional surgeries and overall costs. To optimize coating parameters, this study employs RSM and ML techniques, including genetic algorithm (GA), particle swarm optimization (PSO), random search optimization (RSO), and differential evolution (DE). Experimental validation of the optimized process parameters and their corresponding fitness values is carried out using both RSM and ML approaches. The results demonstrate that GA has the closest relationship between experimental and fitness values, followed by DE, RSM, PSO, and RSO. Highlights Direct immersion coating of polydopamine on 3D printed PLA bone plates. Evaluating mechanical strength for bone plates coated at varying parameters. Statistical modeling and optimization of mechanical strength using RSM. Mechanical strength optimization and convergence properties for ML models. Experimental validation of RSM and ML‐based optimization algorithms.

The increasing adoption of solar energy as a clean and sustainable power source has intensified research efforts toward developing more efficient photovoltaic (PV) cells. These cells exhibit nonlinear characteristics that are significantly influenced by variations in irradiance and temperature. Accurate estimation of PV model parameters plays a crucial role in maximizing performance, particularly via precise maximum power point (MPP) tracking. This paper presents a new hybrid metaheuristic algorithm that combines the global exploration capability of the Bat Algorithm (BA) with local exploitation efficiency of the Crow Search Algorithm (CR) to optimize PV parameter estimation. The proposed approach is tested using Single-Diode (SDM), Double-Diode (DDM), and Triple-Diode (TDM) models based on the RTC France dataset. The hybrid model demonstrates better convergence behavior and robustness compared to conventional approaches. Qualitatively, it effectively manages parameter uncertainty; quantitatively, it achieves RMSE values of 0.00077299 (SDM), 0.0008215 (DDM), and 0.0008068 (TDM), outperforming traditional algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).

Non-convex optimization problems are prevalent in scientific and engineering applications, often characterized by multiple local minima that challenge standard optimization techniques. Global optimization methods, such as Genetic Algorithms (GA) (1992) [4] and Particle Swarm Optimization (PSO) (1995) [6], provide robust global exploration but tend to converge slowly. Conversely, local search methods, such as Newton’s method (2004) [1], (2006) [7], and the Conjugate Gradient (CG) (1964) [3] method, offer rapid convergence but are susceptible to local minima. In this paper, we present four hybrid optimization algorithms that integrate the exploratory strength of global algorithms with the refinement ability of local methods. The proposed hybrids, Genetic Algorithms (GA) + Newton, GA + CG, PSO + Newton, and PSO + CG, are evaluated against their standalone counterparts on standard benchmark functions (2013) [5], including Rosenbrock, Rastrigin, Ackley, and Himmelblau. This work builds upon our previous research on the hybridization of two global search algorithms, the Nelder–Mead (simplex) algorithm and the Bat algorithm, which resulted in the Hybrid Simplex Bat Algorithm (HSBA) [2], recently Published. Results demonstrate that the hybrid approaches consistently outperform traditional methods in terms of accuracy, convergence rate, and computational efficiency.

1. In this study, neural network (NN) and response surface (RS) models were developed to investigate the response [average daily gain (ADG) and feed efficiency (FE)] of young broiler chickens to dietary protein and lysine. For this purpose, data on their responses to dietary protein and lysine were extracted from the literature and separate NN and RS models were constructed. 2. Comparison between the NN and RS models revealed higher accuracy of prediction with the NN models compared to the RS models. In terms of R 2 values, the NN models developed for both ADG (R 2 = 0.923) and FE (R 2 = 0.904) were far superior to the RS models (R 2 for ADG = 0.511; R 2 for FE = 0.67). This suggests that the NN models can serve as an alternative option to conventional regression approaches including use of RS models. 3. Optimisation of the NN models developed for response to protein and lysine showed that diets containing 220.7 (g/kg of diet) protein and 12.85 (g/kg of diet) lysine maximise ADG, whereas maximum FE is achieved with diets containing 241.3 and 13.12 (g/kg) protein and lysine, respectively. Based on the optimisation results, optimal dietary protein and lysine concentrations for maximum FE in broiler chickens during the starting period are higher than for ADG.

In this paper, an improved magnetic equivalent circuit (MEC) is applied to calculate the nonlinear magnetic field in an interior-type permanent-magnet (IPM) brushless DC (BLDC) motor. Compared with the finite element method, the MEC method is much more time efficient, whereas compared with the conventional MEC method, the improved MEC is more accurate since it takes the complicate topological structure of the motor into account. A rough design of the IPM BLDC motor was firstly conducted by the improved MEC method. The particle swarm optimization (PSO) algorithm is then employed to refine the design for optimal structural parameters that result in the lowest cost and highest performance.

Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization are of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR, and RSM models, respectively.