Mohd Saufi Ahmad
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Preferred name
Mohd Saufi Ahmad
Official Name
Mohd Saufi , Ahmad
Alternative Name
Ahmad, M. S.
Ahmad, Mohd Saufi
Main Affiliation
Scopus Author ID
57214108987
Researcher ID
AAK-3467-2020

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  • Publication
    Optimization Design of the Electromagnetic Torque for Surface-Mounted PMSM Using GA and Finite Element Analysis for Electric Vehicle
    ( 2022-09-01)
    Wong E.W.M.
    ;
    Mohd-Shafri S.A.
    ;
    Tiang Tow Leong
    ;
    Tan C.J.
    ;
    Ishak D.
    ;
    Mohd Saufi Ahmad
    ;
    Leong J.H.
    ;
    Ong Hui Lin
    This research offers an optimization design for a three-phase surface-mounted permanent magnet synchronous machine (SMPMSM) by employing the computer framework consists of finite element analysis (FEA) and genetic algorithm (GA) utilized in the applications of electric vehicles. This framework aims to evaluate and determine the optimal setting of SMPMSMs, which require maximum average electromagnetic torque (Tem_avg) and minimum its ripple (Tem_rip). Several motor performances, such as magnetic field distribution across the motor, magnetic flux density distribution in the mid air gap, phase back-EMF, electromagnetic torque, and its ripple, are investigated for the initial and optimal designs of PM machines during open-circuit and on-load conditions by using FEA. The important parameter of PM machines, i.e. the magnet pole-arc and slot opening angle are taken into account. In order to determine the objective function of the GA framework, the Tem_avg and the Tem_rip are used to formulate the computing equations where the fitness value provided by the computing framework is further assessed. The GA framework is used to assess the comparison of parameters and motor performance between the initial and optimal designs of 12s/8p PM motors in terms of electromagnetic torque under BLAC operation. Consequently, the framework of FEA and GA has been proven in the design of SMPMSM, which is very viable for electric cars by reducing the magnet materials and electromagnetic torque ripple.
  • Publication
    The Influenced of Different Magnetization Patterns on the Performance of the Semi-buried Permanent Magnet Synchronous Machine
    ( 2021-06-11)
    Syauqina Akmar Mohd-Shafri
    ;
    Muhamad Haniff Sani
    ;
    Tiang Tow Leong
    ;
    Ishak D.
    ;
    Mohd Saufi Ahmad
    ;
    Leong Jenn Hwai , Jenn
    ;
    Tan C.J.
    ;
    Ong Hui Lin
    The performance of semi-buried permanent magnet synchronous machines (SBPMSMs) by the influence of two magnetization patterns are presented in this paper. These magnetization patterns include radial and parallel, which applied into 9-slot/8-pole (9s/8p) and 6-slot/4-pole (6s/4p) SBPMSMs. Hence, to evaluate the machines performance, AutoCAD and Opera2D finite element software are used to model and predict the electromagnetic characteristic performance of SBPMSMs. Two PM machines are optimized i.e. flux density distribution, phase back-EMF, and cogging torque by two magnetization patterns. The phase back-EMF of the machines are computed into harmonic components to investigate the total harmonic distortion (THDv ). It is found that the lowest THDv for both 9s/8p and 6s/4p motors are in parallel magnetization (PaM), which are 8.66% and 3.98%, respectively. However, the lowest cogging torque for 9s/8p is radial magnetization (RaM), which is 0.0101 Nm and for 6s/4p is 0.1730 Nm with parallel magnetization pattern. By comparing the result of the optimum magnet pole arc for both motors, the 6s/4p motors show the minimum cogging torque and harmonic distortions are 0.16 Nm and 1.63% in PaM patterns. As a result, optimum motor performances among these two motors are 6s/4p PM motors with PaM pattern.
      2
  • Publication
    Optimal Design of SMPMSM Using Genetic Algorithm Based on Finite Element Model
    ( 2022-01-01)
    Mohd-Shafri S.A.
    ;
    Tiang Tow Leong
    ;
    Ishak D.
    ;
    Tan C.J.
    ;
    Mohd Saufi Ahmad
    ;
    Leong J.H.
    ;
    Ong Hui Lin
    This paper deals with an optimal design of a surface-mounted permanent magnet synchronous machine (SMPMSM) using a genetic algorithm (GA) method. To analyze the characteristic of permanent magnet (PM) motors, the classical optimization method, such as the finite element method (FEM), is intensively used. In this design, a three-phase 12-slot/8-pole PM motor is established with FEM with radial magnetization pattern. Then, the GA is used to search the optimality of SMPMSM machine design. In the final analysis, the optimal new design of SMPMSM is demonstrated by comparing it with the initial design that is investigated by FEM. The result of induced back-EMF, total harmonic distortion, and magnetic flux density of optimal design are compared with the initial design to show the advantages of GA optimization method.
  • Publication
    Aerial platforms to ensure communications reliability in disaster areas
    ( 2014)
    Satea Hikmat Alnajjar
    ;
    Fareq Malek
    ;
    Mohd Sharazel Razalli
    ;
    Mohd Saufi Ahmad
    Wireless mobile networking technology can be used to reduce a negative effect that occurs in disaster areas. This study, will present an innovative technique via utilize a Low-altitude platform, in order to provide expanded coverage. There are still some challenges to be dealt with in the current manner, e.g., changeableness in direction, which can lead to the loss of a permanent connection between network node's deployments. Another problem can be attenuation in a communication channel due to bad weather factors. Aerial platform is a sky-base station that utilizes free-space optics (FSO) to connect network nodes in addition to meeting the demand of service quality to the “last mile.” A new approach is used to connect several aerial platforms that are helpful in increasing the range of the network deployment. Therefore, the purpose of this study is to examine the performance of these platforms by connecting multiple-network nodes in various weather environments.
  • Publication
    Design and Optimization of Electromagnetic Torque for a Surface-Mounted PMSM by using Subdomain Model and GA in Electric Vehicle Application
    ( 2021-01-01)
    Syauqina Akmar Mohd Shafri
    ;
    Tiang Tow Leong
    ;
    Tan C.J.
    ;
    Ishak D.
    ;
    Mohd Saufi Ahmad
    ;
    Leong Jenn Hwai , Jenn
    ;
    Ong Hui Lin
    This paper presents a highly structured optimization strategy for 12-slot/8-pole surface-mounted permanent magnet synchronous machines (SMPMSM) with radial magnetization pattern. The main goals of the optimization process are to determine the optimal motor geometry, and achieving the minimum torque ripple and maximum average electromagnetic torque, simultaneously. The first function is used to obtain the maximum electromagnetic torque, and thus obtaining higher efficiency and the second function is used to achieve the minimum torque ripple. A computing framework that ensemble the genetic algorithm (GA) and subdomain model (SDM) with weighted equations is proposed to determine the optimal design of SMPMSM. After the optimization process, the optimal design of PM motor demonstrates much lower torque ripple with reasonable electromagnetic torque as compared with that of the initial design. The comparison of parameters and motor performances between the initial and the optimal designs of 12-slot/8-pole PM motors with the computing framework are validated in terms of electromagnetic torque under BLAC operation using SDM. Thus, the framework of SDM and GA is verified in reducing the usage of magnet materials and the electromagnetic torque ripple of SMPMSM, which is highly viable for electric vehicle. Therefore, the proposed ensemble framework of GA and SDM can determine the optimal settings of geometry design for SMPMSM in order to produce optimum motor performance.
      1
  • Publication
    Analytical Subdomain Model for Double-Stator Permanent Magnet Synchronous Machine with Surface-Mounted Radial Magnetization †
    ( 2021-01-01)
    Mohd Saufi Ahmad
    ;
    Ishak D.
    ;
    Tiang Tow Leong
    ;
    Mohamed M.R.
    This paper proposes an analytical subdomain model for predicting magnetic field distributions in a three-phase double-stator permanent magnet synchronous machine (DS-PMSM) during open-circuit and on-load conditions. The geometric structure of DS-PMSM is quite challenging since the stator cores are located in the outer and inner parts of the motor, while the rotor magnets are placed between these two stators. Parameters that influence the motor performance in DS-PMSM include stator outer radius, stator inner radius, magnet thickness, magnet arc, slot opening, outer and inner airgap thickness and the number of winding turns. The analytical subdomain model proposed in this paper, which can accurately predict the performances of DS-PMSM with less computational time, has an excellent advantage as a rapid design tool. The model is initially generated using the separation of variables technique in four subdomains, namely, outer airgap, outer magnet, inner magnet, and inner airgap, based on Laplace’s and Poisson’s equations in polar coordinates. The field solutions in each subdomain are derived by applying the appropriate boundary and interface conditions. Furthermore, finite element analysis (FEA) is used to validate the analytical results in fractional DS-PMSM with a different number of slots between outer and inner stators and a non-overlapping winding configuration. The electromagnetic performances that have been evaluated are the slotted airgap flux density, back-emf and output torque. The results demonstrate that the proposed analytical model is able to predict the magnetic field distributions accurately in DS-PMSM.
      1
  • Publication
    Optimal Design of SMPMSM Using SD-model based on Genetic Algorithm
    ( 2021-01-01)
    Syauqina Akmar Mohd-Shafri
    ;
    Tiang Tow Leong
    ;
    Tan C.J.
    ;
    Ishak D.
    ;
    Mohd Saufi Ahmad
    ;
    Leong Jenn Hwai , Jenn
    ;
    Ong Hui Lin
    This paper deals with an optimal design of a surface-mounted permanent magnet synchronous machine (SMPMSM) with an exact analytical subdomain model by using a genetic algorithm method. To analyze the characteristic of permanent magnet (PM) motors, the classical optimization method, such as the finite element method (FEM), is intensively used. However, FEM has several time problems that require a longer computational time to evaluate the performance of PM motors. This problem can be overcome by using a genetic algorithm (GA) method combined with a subdomain model (SD), which developed an improved performance of SMPMSM, for instance, total harmonic distortion (THDv) and cogging torque. In this design, a three-phase 12-slot/8-pole PM motor is established with an exact SD model with RM and PaM magnetization patterns. Then, the GA ensemble with SD model to search the optimality of SMPMSM machine design. In the final analysis, the optimal new design of SMPMSM demonstrated by comparing with the initial design that is investigated by FEM. The result of induced back-EMF, cogging torque, total harmonic distortion, and magnetic flux density of optimal design is compared with the initial design to show the advantages of GA optimization method.
      1
  • Publication
    Optimization Design of the Electromagnetic Torque for Surface-Mounted PMSM using GA and Finite Element Analysis for Electric Vehicle
    ( 2021-01-01)
    Edric Wong Wee Ming
    ;
    Tan C.J.
    ;
    Leong Jenn Hwai , Jenn
    ;
    Syauqina Akmar Mohd-Shafri
    ;
    Ishak D.
    ;
    Ong Hui Lin
    ;
    Tiang Tow Leong
    ;
    Mohd Saufi Ahmad
    This paper presents an optimization design for a three-phase 12 mathrm{s}/8 mathrm{p} surface-mounted permanent magnet synchronous machine (SMPMSM) with a RM pattern applied in the electric vehicle by using the computing framework of finite element analysis (FEA) and genetic algorithm (GA). The framework is to determine the optimal settings of permanent magnet (PM) motor for higher average electromagnetic torque (T_{em_{-}avg}) and lower its ripple (T_{em_{-}rip}). Several motor performances are investigated for the initial and the optimal designs of PM machines during open-circuit and on-load conditions, i.e., magnetic field distribution across the motor, magnetic flux density distribution in the mid air gap, phase back-EMF, electromagnetic torque, and its ripple through FEA. The magnet pole-arc and slot opening angle of the PM motor are taken into consideration. The T_{em_{-}avg} and the T_{em_{-}rip} are employed to formulate the computing equations to determine the objective function, which is used to search the optimal settings of PM motor in GA framework, where the fitness value produced by the computing framework is further evaluated. The comparison of parameters and motor performances between the initial and the optimal designs of 12s/8p PM motors with the GA framework are validated in terms of electromagnetic torque under BLAC operation using FEA. Therefore, the framework of FEA and GA is verified in reducing the usage of magnet materials and the electromagnetic torque ripple in the design of SMPMSM, which is highly viable for electric vehicle.
      1
  • Publication
    Optimization of double stator pmsm with different slot number in inner and outer stators using genetic algorithm
    ( 2021-06-01)
    Mohd Saufi Ahmad
    ;
    Ishak D.
    ;
    Tiang Tow Leong
    ;
    Mohamed M.R.
    This paper describes the performance enhancement of double stator permanent magnet synchronous machines (DS-PMSM) based on genetic algorithm optimization (GAO). Generally, throughout the development stage, an analytical calculation is implemented to build the initial model of the DS-PMSM since the analytical calculation can provide the initial parameters based on the types and materials used in the machine design. For further improvement, GAO might potentially be applied to provide the optimization technique in searching the optimal motor parameters iteratively and intelligently with specific objective functions. For this aim, a three-phase, DS-PMSM with different number of slots between the outer and inner stators is first designed by using analytical parameter estimation and then later optimized by GAO. The outer and inner stators have 12-slots and 9-slots respectively, while, the rotor carries 10 magnetic poles. Four main input motor parameters, i.e. outer stator slot opening, outer magnet pole arc, inner stator slot opening and inner magnet pole arc are varied and optimized to achieve the design objective functions, i.e. high output torque, low torque ripple, low cogging torque and low total harmonic distortion (THDv). The results from the optimized GAO are compared with the initial motor model and further validated by finite element method (FEM). The results show a good agreement between GAO and FEM. GAO has achieved very significant improvements in enhancing the machine performance.
      1