• Proceedings of the KIPE Conference
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• 1998.10a
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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.

• Journal of Power Electronics
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• v.12 no.3
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• pp.468-476
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• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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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. 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.93-100
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• 2018

This study proposes a controller that compensates torque error for precise angular velocity tracking and a method to compensate the stability of controller in implementation. Also, it is proved that the designed controller can be asymptotically stable based on Lyapunov stability theory. The proposed controller is able to control the d-axis reference current to arbitrary values and easily achieve control performance with two gains. As a result of applying to IPMSM of about 750W class, the steady state error with reference speed 1200 [RPM] is within 0.1 [%]. And it can be seen that it is an asymptomatic stable controller overcoming disturbance within about 0.2 second in application of constant load of about 5 [Nm].

• Journal of Magnetics
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• v.18 no.4
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• pp.487-493
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• 2013

The automotive industry is showing widespread interest in belt-driven electric motor-assisted (e-Assist) systems. A belt-driven assist system (BAS) starts and assists the combustion engine in place of the conventional generator. In this study, a water-cooled wound rotor synchronous motor (WRSM) for the e-Assist system was designed and analyzed. The performance of the WRSM was compared with that of an interior permanent magnet synchronous motor (IPMSM). The WRSM efficiency can be improved for the BAS by adjusting the field flux at high speeds. The field current map to obtain the maximum efficiency based on the speed and torque was developed. To control the field flux via field current control in the WRSM, a general H-bridge circuit was added to the WRSM inverter to get the rapid current response in the high-speed region; the characteristics were compared with the chopper circuit. A WRSM developed for the belt-driven e-Assist system and a prototype 115 V power electronic converter to drive the WRSM were tested with a 900 cc combustion engine. The test results showed that the WRSM-type e-Assist system had good characteristics and could successfully start and assist the 900 cc combustion engine.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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• pp.1125-1131
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• 2013

Nowadays, embedded software development using the MATLAB/Simulink system is gradually emerging. Studies generating the parts of embedded S/W in a Rapid Prototype are presented. In this paper, a method to generate the entire embedded S/W of enhanced AC motor control is proposed. High performance motor control could not be achieved with the basic Simulink library and RAppID Toolbox library as it does not have PWM based Interrupt, an ASAC (Analog Sensing for AC Motors) function and other special functions of the Freescale MPC555x. Consequently, the required libraries for enhanced AC motor control are created by Legacy code tool, TLC (Target Language Compiler) and S-Function (System-Function) of MATLAB/ Simulink and utilized in the Rapid Prototype. Motor control performance and execution time are compared automatically to the generated-code S/W with the hand coded S/W. The IPMSM (Interior Permanent Magnet Synchronous Motor) and MPC5553 board that were designed as the AC motor controller for hybrid electrical vehicle are used for the test. The performances meet the requirements and satisfactory results are acquired.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.752-760
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• 2018

In this paper, interior permanent magnet synchronous motor (IPMSM) design for sensorless drive, considering magnetic neutral point shift according to magnetic saturation, has been proposed. Sensorless control was divided into a method based on inductance and a method based on back induced voltage. Because induced voltage is very small at zero or low speed, error in rotor initial position estimation may occur. Using the ratio of saliency addresses this problem. When using high-frequency injections at low speed, the rotor's initial position is estimated at the smallest portion of the inductance. IPMSM has the minimum inductance at the d-axis. However, if magnetic saturation leads to magnetic neutral point variation, following the load current change, there is a change in the minimum point of inductance. In this case, it can lead to failure of initial rotor position estimation. As a result, it is essential that the blocking design has an inductance minimum point shift. As such, in this study, an IPMSM design method, by blocking magnetic neutral point change, has been proposed. After determining the inductance profile based on the finite element analysis (FEA), the results of proposed method were verified.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.5
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• pp.402-409
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• 2015

This paper proposes a novel rotor position error calculation method for high-frequency signal-injected sensorless control. The rotor position error using the conventional modulation method can be only measured up to ${\pm}45^{\circ}$. In addition, when the rotor position estimation error is not sufficiently small, the small angle approximation in no longer valid. To overcome these problems, this study introduces a new rotor position error calculation method using the rotating matrix. In this study, the position error measurement range of the proposed method is extended from ${\pm}45^{\circ}$ to ${\pm}90^{\circ}$. The linearity between the real rotor position error and the estimated error is maintained by nearly $90^{\circ}$. These features of the proposed method improve the performance of the sensorless control. The validity of the proposed method is verified by simulations and experiments.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.110-114
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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. 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.

• Journal of the Korean Society for Railway
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• v.11 no.4
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• pp.398-403
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• 2008

The propulsion for locomotive application has changed from the DC motor system to the induction motor system. Although the induction motor system has almost reached the stage of maturity, this system also needs to be changed to the PM motor system for the direct drive without using reduction gear. Thus, the IPMSM (Interior buried Permanent Magnet Synchronous Motor) has been adopted to meet the locomotive driving specification. Where the wheel is directly dirven by the traction motor. In this paper, the investigation on IPMSM satisfying driving specifications for the direct drive has been performed using the advanced FEM.