Tiang Tow Leong
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Tiang Tow Leong
Official Name
Tiang, Tow Leong
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Scopus Author ID
53985359200
Researcher ID
ABT-7481-2022

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  • Publication
    Investigation the optimum performance of the surface-mounted PMSM under different magnetization patterns
    ( 2020-01-07)
    Akmar Mohd-Shafri, Syauqina
    ;
    Tiang Tow Leong
    ;
    Ishak D.
    ;
    Leong Jenn Hwai , Jenn
    ;
    Jun Tan C.
    ;
    Ong Hui Lin
    This paper investigates the influence of different magnetization patterns on the performances of the surface-mounted permanent magnet synchronous machines (SMPMSMs). Three magnetization patterns are employed, which are radial, parallel, and ideal Halbach magnetizations. These magnetization patterns are applied to 9-slot/10-pole and 15-slot/4-pole permanent magnet (PM) machines. The PM machines are designed and simulated by using Opera 2D finite element. The performances of three PM motors, such as airgap flux density, phase back-EMF, and cogging torque, are evaluated under the influence of different magnetization patterns. The total harmonic distortion of phase back-EMF (THDv) for the motors are investigated. The PM motors with ideal Halbach magnetization provide the lowest cogging torque and the lowest total harmonic distortion of phase back-EMF. Besides that, the optimum setting of the magnet pole-arc can reduce the total harmonic distortion of phase back-EMF and achieve lower cogging torque. The optimum magnet pole-arc produced by radial magnetization in 9-slot/10-pole motor is 24.8 mech., with cogging torque of 0.45 Nm, and THDv of 2.69 %. Meanwhile, the optimum magnet pole-arc produced by parallel magnetization in 9-slot/10-pole motor is 26.0 mech., with cogging torque of 0.41 Nm, and THDv of 2.00 %.
      12  40
  • 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