• 전력전자학회논문지
• /
• 제8권3호
• /
• pp.221-229
• /
• 2003

IPMSM은 고효율, 고성능으로 전동기로서 전기 자동차의 적용분야에 적용되고 있다. 본 논문에서는 전기자동차의 구동을 위하여 PMSM의 약계자 영역에서 최대 토크제어를 제시한다. 최대 토크동작을 위하여 최적 d축 전류를 결정하고 이 전류를 각 제어모드에서 사용한다. 최대 토크를 발생하기 위하여 전류 조절기의 출력인 인버터의 출력 전압은 DC 링크전압을 최대로 이용한다. 제안한 IPMSM 최대 토크제어의 특성을 제시하고 타당성을 입증한다.

• 전력전자학회:학술대회논문집
• /
• 전력전자학회 2010년도 하계학술대회 논문집
• /
• pp.444-445
• /
• 2010

This paper deals with the development of an IPMSM motor drive for NEVs. The drive system consists of 7kW IPMSM motor, DSP-based controller, and MOSFET-based inverter. The controller provides the same accelerator function as the conventional vehicle for an easy NEV drive.

• 조명전기설비학회논문지
• /
• 제19권8호
• /
• pp.85-93
• /
• 2005

본 논문에서는 고속 드라이브를 위하여 IPMSM의 약계자 영역에서 최대 토크제어를 제시한다. 최대 토크동작을 위하여 최적 d축 전류를 결정하고 이 전류를 각 제어모드에서 사용한다. 최대 토크를 발생하기 위하여 전류 조절기의 출력인 인버터의 출력전압은 DC 링크전압을 최대로 이용한다. 제어모드의 원활한 전이는 지령신호에 기초하여 자동적으로 수행한다. 본 논문에서 제시한 최대 토크제어로 IPMSM 드라이브에 적용시험을 한다. 그리고 시험결과의 응답특성을 다양하게 분석하여 본 논문의 타당성을 입증한다.

• 한국조명전기설비학회:학술대회논문집
• /
• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
• /
• pp.420-423
• /
• 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 is proposed 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.

• 조명전기설비학회논문지
• /
• 제23권8호
• /
• pp.57-66
• /
• 2009

종래의 고정된 이득을 가진 PI 제어기는 지령속도, 부하변화 등과 같은 파라미터 변동에 대해서 매우 민감하다. IPMSM 드라이브의 정확한 속도제어는 비선형적인 전자기적 발생저항뿐만 아니라 회전자 속도와 권선저항사이의 비선형적 관계 때문에 복잡한 문제점이 있다. 따라서 광범위한 동작상태에서 최적 제어를 위해 PI 제어기의 이득값을 실시간으로 조절해야한다. 본 논문은 FNN과 ALM을 이용하여 IPMSM 드라이브의 HIPI 제어기를 제시한다. 제시된 제어기는 ANN을 이용하여 속도를 추정하고, 시스템 외란에 대해서 IPMSM 드라이브의 고성능 속도제어를 제시한다. PI 제어기의 이득값은 모든 동작상태에서 ALM-FNN에 의해 최적화 되어진다. 제시된 제어기는 다양한 동작상태에 대한 분석을 통해 타당성을 입증한다.

• 전력전자학회:학술대회논문집
• /
• 전력전자학회 2013년도 전력전자학술대회 논문집
• /
• pp.441-442
• /
• 2013

This paper deals with 100kW IPMSM drive inverter for Midium Duty Electric Car. First, it introduces about hardware structure of inverter. Then performance of IPMSM drive system is confirmed through experiments.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
• /
• pp.566-569
• /
• 2005

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using artificial intelligent(AI) controller. The control method is applicable over the entire speed range and 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 AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) 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 proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also. this paper is proposed the experimental results to verify the effectiveness of AI controller.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
• /
• 제55권2호
• /
• pp.89-97
• /
• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and 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 LM-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 is proposed speed control of IPMSM using LM-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 LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제55권3호
• /
• pp.110-114
• /
• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. The control method is applicable over the entire speed range and 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 AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) 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 proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.

• 산업기술연구
• /
• 제33권A호
• /
• pp.61-66
• /
• 2013

This paper is about the development of 80[kW] IPMSM(Interior Permanent Magnet Synchronous Motor) drive system for an electric vehicle. MTPA(Maximum Torque per Ampere) operation and flux-weakening operation for the optimal torque control of the IPMSM are presented. In this system, the torque control of the IPMSM is achieved by using the look-up table, which gives d- and q-aixs current references for the given torque command in the MTPA operation and flux-weakening operation regions. This look-up table is made by current injection tests, and from which the motor parameters are also estimated. The proposed system is verified by the experiment on the electric vehicle drive system, which consists of an 80[kW] IPMSM and an IGBT inverter.