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
    Optimal design of SMPMSM using Genetic Algorithm based on Finite Element Model
    (Springer Science and Business Media Deutschland GmbH, 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.
      37  4
  • Publication
    Optimization design of the electromagnetic torque for Surface-Mounted PMSM using GA and Finite Element Analysis for electric vehicle
    (Korean Institute of Electrical Engineers, 2022-09-01)
    Wong Edric Wee Ming
    ;
    Mohd-Shafri Syauqina Akmar
    ;
    Tiang Tow Leong
    ;
    Choo Jun Tan
    ;
    Dahaman Ishak
    ;
    Mohd Saufi Ahmad
    ;
    Leong Jenn Hwai
    ;
    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.
      11  26
  • Publication
    Optimization design of the electromagnetic torque for surface-mounted PMSM using GA and finite element analysis for electric vehicle
    (IEEE, 2021-01-01)
    Edric Wong Wee Ming
    ;
    Choo Jun Tan
    ;
    Leong Jenn Hwai
    ;
    Syauqina Akmar Mohd-Shafri
    ;
    Dahaman Ishak
    ;
    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
    Design and optimization of electromagnetic torque for a surface-mounted PMSM by using subdomain model and GA in electric vehicle application
    (IEEE Press, 2021-01-01)
    Syauqina Akmar Mohd Shafri
    ;
    Tiang Tow Leong
    ;
    Choo Jun Tan
    ;
    Dahaman Ishak
    ;
    Mohd Saufi Ahmad
    ;
    Leong Jenn Hwai
    ;
    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