• Title, Summary, Keyword: IPMSM

Search Result 501, Processing Time 0.05 seconds

A Study on Feedforward Compensation Method of IPMSM for EV with Non-sinusoidal BEMF (비 정현파 역기전압을 가지는 EV용 IPMSM의 전향보상 제어기법에 관한 연구)

  • Park, Gui-Yeo;Park, Jung-Woo;Ahn, Won-Il;Shin, Duck-Woong;Jeong, Moon-Seon;Moon, Chae-Joo
    • The Transactions of the Korean Institute of Power Electronics
    • /
    • v.18 no.6
    • /
    • pp.573-578
    • /
    • 2013
  • In the case of the Back EMF voltage contains the harmonics, the motor torque ripple and vibration is occurred by the current pulsation, because IPMSM control algorithm is the model which is assumed that it contains a sinusoidal Back EMF voltage. To improve ride quality, in the case of IPMSM for EV, improving the torque control characteristics is necessary. Therefore, there is a need to minimize the influence of the harmonics. In this paper, the investigation to decrease the current distortion factor has been performed for improving torque control characteristics by applying the non-sinusoidal Back EMF to IPMSM model.

High Performance Speed Control of IPMSM with LM-FNN Controller (LM-FNN 제어기에 의한 IPMSM의 고성능 속도제어)

  • Nam, Su-Myeong;Choi, Jung-Sik;Chung, Dong-Hwa
    • The Transactions of the Korean Institute of Power Electronics
    • /
    • v.11 no.1
    • /
    • pp.29-37
    • /
    • 2006
  • Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and ANN(artificial neural network) control. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility md numerical processing capability. Also, this paper proposes speed control of IPMSM using LM-FNN and estimation of speed using artificial neural network controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. 'The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Analysis results to verify the effectiveness of the new hybrid intelligent control proposed in this paper.

Speed Control of IPMSM Drive using NNPI Controller (NNPI 제어기를 이용한 IPMSM 드라이브의 속도 제어)

  • Jung, Dong-Wha;Choi, Jung-Sik;Ko, Jae-Sub
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
    • /
    • v.20 no.7
    • /
    • pp.65-73
    • /
    • 2006
  • This paper presents speed control of IPMSM drive using neural network(NN) PI controller. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, NNPI controller proposes a new method based neural network. NNPI controller is developed to minimize overshoot rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

HIPI Controller of IPMSM Drive using ALM-FNN (ALM-FNN을 이용한 IPMSM 드라이브의 HIPI 제어기)

  • Ko, Jae-Sub;Choi, Jung-Sik;Chung, Dong-Hwa
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
    • /
    • v.23 no.8
    • /
    • pp.57-66
    • /
    • 2009
  • The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper proposes hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme, The validity of the proposed controller is verified by results at different dynamic operating conditions.

Efficiency Optimization Control of IPMSM using Neural Network (신경회로망을 이용한 IPMSM의 효율 최적화 제어)

  • Chol, Jung-Sik;Ko, Jae-Sub;Chung, Dong-Hwa
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
    • /
    • v.22 no.1
    • /
    • pp.40-49
    • /
    • 2008
  • Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications and so of due to their excellent power to weight ratio. To obtain maximum efficiency in these applications, this paper proposes the neural network control method. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the error back propagation algorithm(EBPA) of neural network. The minimization of loss is possible to realize eHciency optimization control for the IPMSM drive. This paper proposes high performance and robust control through a real time calculation of parameter variation such as variation of back emf constant, armature resistance and d-axis inductance about the motor operation. Proposed algorithm is applied IPMSM drive system, prove validity through analysis operating characteristics con011ed by efficiency optimization control.

Maximum Torque Control of IPMSM with Adaptive Learning Fuzzy-Neural Network (적응학습 퍼지-신경회로망에 의한 IPMSM의 최대토크 제어)

  • Ko, Jae-Sub;Choi, Jung-Sik;Lee, Jung-Ho;Chung, Dong-Hwa
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
    • /
    • /
    • pp.309-314
    • /
    • 2006
  • Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

  • PDF

On-line Efficiency Optimization of IPMSM drive using Fuzzy Control and Loss Minimization Method (퍼지제어와 손실최소화 기법을 이용한 IPMSM 드라이브의 실시간 효율최적화 제어)

  • Kang, Seong-Jun;Ko, Jae-Sub;Jang, Mi-Geum;Kim, Soon-Young;Mun, Ju-Hui;Lee, Jin-Kook;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
    • /
    • /
    • pp.1356-1357
    • /
    • 2011
  • Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes on-line efficiency optimization of IPMSM drive using fuzzy logic control(FLC) and the loss minimization method. In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. The proposed control algorithm is applied to IPMSM drive system and the operating characteristics controlled by the loss minimization method and FLC control are examined in detail.

  • PDF

Efficiency Optimization Control of IPMSM Drive using multi HFC (다중 HFC를 이용한 IPMSM 드라이브의 효율 최적화 제어)

  • Choi, Jung-Sik;Ko, Jae-Sun;Kang, Sung-Jun;Baek, Jeong-Woo;Jang, Mi-Geum;Kim, Soon-Young;Chung, Dong-Hwa
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
    • /
    • /
    • pp.355-358
    • /
    • 2009
  • This paper proposes efficiency optimization control of IPMSM drive using multi hybrid fuzzy controller(HFC). The design of the speed controller based on fuzzy-neural network that is implemented using fuzzy control and neural network. The design of the current based on HFC using model reference and the estimation of the speed based on neural network using ANN controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using multi HFC. Also, this paper proposes speed control of IPMSM using HFC1, current control of HFC2-HFC3 and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled HFC, the operating characteristics controlled by efficiency optimization control are examined in detail.

  • PDF

A Study on the Design of Flat-Type IPMSM in Parallel Hybrid Traction Application (병렬형 하이브리드 구동용 매입형 영구자석동기전동기 설계에 대한 연구)

  • Kim Ki-Nam;Yang Hai-Won
    • The Transactions of the Korean Institute of Electrical Engineers B
    • /
    • v.53 no.12
    • /
    • pp.718-724
    • /
    • 2004
  • This study investigates the design factors of Interior Permanent Magnet Synchronous Motor(IPMSM) which is applied to Hybrid electric vehicle as a driving power. Recently, there are many studies of IPMSM for application to Hybrid Electric Vehicle, because IPMSM has characteristics of high torque, high power density and high efficiency which come from reluctance torque due to difference of inductance as well as magnet torque. This study analyzes the inductance and design characteristics of IPMSM by using finite element method and focuses on design and analysis of IPMSM which can operates with high efficiency at low speed range. For this embodiment, magnet shape is changed from conventional block type to arc type without any change of outline dimension of motor and this change of magnet shape makes it possible to increase back EMF and sinusoidal waveform. Analysis results are verified by test of improved and embodied motor. As a test result , increased back EMF and sharply decrease of harmonics are secured and through this contribution of reduced fuel consumption of Hybrid electric vehicle is expected.

Adaptive FNN Controller for Maximum Torque of IPMSM Drive (IPMSM 드라이브의 최대토크를 위한 적응 FNN 제어기)

  • Kim, Do-Yeon;Ko, Jae-Sub;Choi, Jung-Sik;Jung, Byung-Jin;Park, Ki-Tae;Choi, Jung-Hoon;Chung, Dong-Hwa
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
    • /
    • /
    • pp.313-318
    • /
    • 2007
  • Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive fuzzy neural network controller and artificial neural network(ANN). This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using Adaptive-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper reposes speed control of IPMSM using Adaptive-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the Adaptive-FNN and ANN controller.

  • PDF