• 한국산학기술학회논문지
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• 제19권4호
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• pp.578-585
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• 2018

본 논문은 고속철도차량에 전기기계제동장치(EMB : Electric Mechanical Brake)를 적용하기 위한 주요 구성품인 3상 매입형영구자석동기전동기(IPMSM : Interior Permanent Magnet Synchronous Motor)의 설계방법과 이를 이용한 인버터 제어시스템의 압부력제어 시뮬레이션 방법을 제안한다. 최근 자동차에서 주로 사용하는 유압식 제동장치는 유압을 발생시키기 위해 필요한 오일류와 유압 라인의 관리, 유지보수성 및 유압펌프의 동작으로 인한 효율성 등이 문제로 제기되면서 EMB에 대한 관심이 높아지고 있으나 비용증가 및 안전측면의 보완이 지속적으로 요구되고 있다. 공압식 제동장치를 주로 사용하는 철도차량은 EMB 시스템을 적용할 경우 차량 하부에 큰 공간을 차지하는 공기압축기, 제동공기통 및 연결 배관 등의 부품이 필요하지 않으므로 50% 이상의 소형화가 가능하며 인버터를 적용한 전동기 구동방식으로 인하여 상대적으로 빠른 응답속도와 정밀제어를 통해 공주거리를 단축시킬 수 있는 장점을 갖는다. 또한, 철도차량은 다수의 제동장치가 제동력을 분담하는 구조로 설계되어 자동차와 비교하여 EMB 적용이 안전측면에서 유리하다. 본 논문에서는 JMAG을 활용하여 고속철도의 제동 캘리퍼와 제동력 출력에 적합한 모터설계 및 전자계해석을 수행하였다. 제동 압부력 제어 시뮬레이션을 위해 기계구동부는 기존 EMB 시스템에 주로 적용된 볼스크류 형태의 동작방식과는 달리 고속철도차량에 적용된 편심축 회전을 이용한 구동방식으로 모델링하였다. IPMSM 제어를 통한 제동압부력 및 제동력 출력결과는 Matlab/Simulink를 활용하여 JMAG의 IPMSM 모델과 co-simulation을 통해 보였으며 결과의 타당성은 차세대고속철도(HEMU-430X)의 제동사양과의 비교를 통해 검증하였다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.309-314
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• 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.

• 전기전자학회논문지
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• 제26권4호
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• pp.653-658
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• 2022

본 논문에서는 온도 변화로 인한 영구자석 쇄교 자속 변화를 고려한 PMSM 드라이브의 MTPA(Maximum Torque per Ampere) 제어 알고리즘을 제시한다. 영구자석 쇄교 자속은 고정자 쇄교 관측기를 통해 실시간으로 추정되고, 추정된 영구자석 쇄교 자속으로부터 토크 오차 보정 계수가 계산된다. 2차원 MTPA look-up table(LUT)은 토크 오차를 보상하기 위해 영구자석 쇄교 자속 변화를 반영하는 MTPA 전류 궤적을 달성하기 위해 개발되었다. 제안된 PMSM의 MTPA 제어 알고리즘은 시뮬레이션을 통해 검증된다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
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• pp.950-954
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• 1998

This paper describes a development of traction unit for 2-motor driven electric vehicle (EV). The traction unit is consisted with an interior permanent magnet synchronous motor (IPMSM), a reduction gear and an inverter for electric vehicle that is driven by 2 motors without differential gear. For traction unit, prototype IPMSM and inverter have been developed. The IPMSM was designed by CAD program that was developed with both equivalent circuit method and FEM. Also the inverter was developed to drive 2 motors with 6 legs IGBT switches in a control board. The vector control algorithm was implemented with maximum torque control method in the constant torque region and field weakening control method in the constant power region considering inverter capacity. To verify that the traction unit is more high efficiency and has more high power density than a traction unit with induction motor with the same power, we would like to show the results about the design and analysis of the IPMSM and the experiment results about the traction unit.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.709-710
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• 2008

In this paper, we calculated permanent magnetic linkage flux ${\psi}_{\alpha}$ and Ld, Lq parameters of IPMSM and compared two model which has different barrier width. IPMSM has two kinds of torque that reluctance torque and magnetic torque. In constant torque region, using the Maxwell stress tensor method, we calculated the torque and current phase angle ${\beta}$ which has appeared maximum torque. In weakening flux region, we calculated the current phase angle ${\beta}$ which flux ${\psi}_o$ lower than limited flux ${\psi}_{omax}$. From the current phase angle ${\beta}$, we calculated torque by torque equation and compared two model characteristic.

• 전력전자학회논문지
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• 제18권4호
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• pp.367-375
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• 2013

The mechanical vibration of a PM synchronous motor at low speeds due to the back emf harmonics may be serious problems in some application such as MDPS(Motor driven power steering), electric vehicles. In this paper, torque ripple reduction for an interior PM synchronous motor including back emf harmonics is proposed. The dq flux linkage harmonics of the permanent magnet are estimated on real time by using the dq currents of the real system and the model of the MRAS observer. Based on the estimated flux linkage harmonics, the dq harmonic currents for reducing the torque ripples are compensated on the dq reference currents. The estimation of the flux linkage harmonics by the MRAS observer and the torque ripple reduction of the proposed algorithm was verified by the simulation and experiment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.146-148
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications. This paper proposes efficiency optimization control of IPMSM drive using AFLC-FNN(Adaptive Fuzzy Learning Control Fuzzy Neural Network)controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The optimal current can be decided according to the operating speed and the load conditions. This paper proposes speed control of IPMSM using AFLC-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 applied to IPMSM drive system controlled AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• 전기학회논문지
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• 제60권9호
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• pp.1700-1707
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• 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. Since the fuzzy neural network(FNN) is recognized general approximate method to control non-linearities and uncertainties, the development of FNN control systems have also grown rapidly. The FNN controller is compounded of fuzzy and neural network. It has an advantage that is the robustness of fuzzy control and the ability to adapt of neural network. However, the FNN has static problem due to their feed-forward network structure. This paper proposes high performance speed control of IPMSM drive using the recurrent FNN(RFNN) which improved conventional FNN controller. The RFNN has excellent dynamic response characteristics because of it has internally feed-back structure. Also, this paper proposes speed estimation of IPMSM drive using ANN. The proposed method is analyzed and compared to conventional FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• 전자공학회논문지SC
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• 제41권3호
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• pp.39-46
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• 2004

본 논문은 IPMSM 드라이브의 고성능 속도 제어를 위하여 퍼지제어와 신경회로망을 혼합 구성한 적응 FNN 제어기를 제시한다. 적응 FNN 제어기는 기준 모델에 기초한 적응 메카니즘을 적용하여 신경회로망의 고도의 적응제어와 퍼지제어기의 강인성 제어의 장점들을 접목한다. 적응 FNN 제어기의 출력은 FNN 제어기의 출력과 적응 퍼지제어의 출력을 합하여 출력을 얻는다. 적응 FNN 제어기는 다양한 동작조건에서 응답특성을 분석하고 평가한다. 제시한 적응 FNN 제어기의 타당성은 IPMSM 드라이브 시스템에 적용하여 성능 결과로 입증한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.880_881
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• 2009

This paper improves the calculating method about the inductance with the high order frequency coreloss. It is different in which the analyzed inductance for calculating IPMSM and the measured thing for experiment. Due to this phenomenon, the expected input voltage differs from the inquired input voltage for operating the motor. This results from the coreloss margin which have both the 1st order and high order frequency value. For reducing the inductance error, after calculating the equivalence coreloss resistance with having the 1st order frequency Back_EMF and coreloss, designing the inductance with the high order frequency which occurred by the coreloss of high order frequency, and comparing the renovated inductance analysis value with the measured thing.