• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.451-459
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• 2018

This work presents an approach for modeling of electric vehicle considering the vehicle dynamics, drive train, rotational wheel and load dynamics. The system is composed of IPMSM (Interior Permanent Magnet Synchronous Motor) coupled with the wheels through a drive train. Generally, IPMSM is controlled by ordinary PID controllers. Performance of the ordinary PID controller is not satisfactory owing to the difficulties of optimal gain selections. To overcome this problem, a new type of fuzzy logic gain tuner for PID controllers of IPMSM is required. Therefore, in this paper fuzzy logic based gain tuning method for PID controller is proposed and compared with some previous control techniques for the better performance of electric vehicle with an optimal balance of acceleration, speed, travelling range, improved controller quality and response. The model was developed in MATLAB/Simulink, simulations were carried out and results were observed. The simulation results have proved that the proposed control system works well to remove the transient oscillations and assure better system response in all conditions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 에너지변화시스템부문
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• pp.225-227
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• 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 artificial intelligent-PI(AIPI) 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.

• 한국자동차공학회논문집
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• 제23권1호
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• pp.25-33
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• 2015

This paper presents the d-q axis equivalent circuit model of an interior permanent magnet (IPM) which includes the iron loss resistance. The model is implemented to be able to run in real-time on the FPGA-based HIL simulator. Power electronic devices are removed from the motor control unit (MCU) and a separated controller is interfaced with the real-time simulated motor drive through a set of proper inputs and outputs. The inputs signals of the HIL simulation are the gate driver signals generated from the controller, and the outputs are the winding currents and resolver signals. This paper especially presents iron loss prediction which is introduced by means of comparing the torque calculated from d-q axis currents and the desired torque; and minimizing the torque difference. This prediction method has stable prediction algorithm to reduce torque difference at specific speed and load. Simulation results demonstrate the feasibility and effectiveness of the proposed methods.

• 한국자동차공학회논문집
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• 제24권1호
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• pp.99-111
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• 2016

This paper presents an integrated fault diagnosis algorithm for driving motor of In-wheel independent drive electric vehicle. Especially, this paper proposes a method that integrated the high level fault diagnosis and the low level fault diagnosis in order to improve a robustness and performance of the fault diagnosis system. The high level fault diagnosis is performed using the vehicle dynamics analysis and the low level fault diagnosis is carried using the motor system analysis. The validity of the high level fault diagnosis algorithms was verified through $Carsim^{(R)}$ and MATLAB/$Simulink^{(R)}$ cosimulation and the low level fault diagnosis's validity was shown by applying it to a MATLAB/$Simulink^{(R)}$ interior permanent magnet synchronous motor control system. Finally, this paper presents a fault diagnosis strategy by combining the high level fault diagnosis and the low level fault diagnosis.

• 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.

• 조명전기설비학회논문지
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• 제19권8호
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• pp.85-93
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• 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
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.420-423
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• 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.

• 전력전자학회논문지
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• 제22권2호
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• pp.175-180
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• 2017

The measurement of three-phase current is important to control the instantaneous torque of a interior permanent magnet synchronous motor(IPMSM) using a three-phase inverter. Therefore, shunt resistors are used in low-cost motor-driving systems to measure three-phase current instead of additional current sensors that are too expensive for these systems. However, in certain regions of a space vector plane, shunt resistors cannot reconstruct three-phase current in high-speed driving mode. In this paper, predictive current control is used to compensate for the three-phase current in those regions, which results in a reduction of current ripple in a three-shunt sensing inverter(TSSI) and torque ripple in IPMSM.

• 전기학회논문지
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• 제65권2호
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• pp.279-285
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• 2016

In this paper, a cogging torque of IPM(Interior Permanent Magnet)-type BLDC motor is analyzed by FE program and the optimized notch on the rotor surface is designed to minimize the torque ripple. A differential evolution strategy algorithm and a response surface method are employed to optimize the rotor notch. In order to verify the proposed algorithm, an IPM BLDC motor is used, which is 50 kW, 8 poles, 48 slots and 1200 rpm at the rated speed. Its characteristics of the motor is calculated by FE program and 4 design variables are set on the rotor notch. The initial shape of the notch is like a non-symmetric half-elliptic and it is optimized by the developed algorithm. The cogging torque of the final model is reduced to $1.5[N{\cdot}m]$ from $5.2[N{\cdot}m]$ of the initial, which is about 71 % reduction. Consequently, the proposed algorithm for the cogging torque reduction of IPM-type BLDC motor using the rotor notch design seems to be very useful to a mechanical design for reducing noise and vibration.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1075-1076
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• 2007

The HEV (Hybrid Electrical Vehicle) becomes commercialized recently because of high fuel efficiency and low air pollution. The highest output power system except the traction motor is an air conditioner compressor in HEV system. The full or hybrid electric compressor is applied for HEV. The general HEC (Hybrid Electric Compressor) requires the half power motor and drive system of the full electric compressor because the rated output power of motor drive system is designed to charge the minimum cooling capacity at the time of idle stop. Therefore, this hybrid electric is more economical and practical solution. In this paper, we studied about the motor drive system of hybrid electric compressor for HEV. The applied voltage specification is 42 V, an IPMSM (Interior Permanent Magnet Synchronous Motor) is designed and applied as the compressor drive motor.