No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Optimum Speed Control of Permanent Magnet Synchronous Motor using Artificial Neural Network-Based Field-Oriented Controller
ResearchGate has not been able to resolve any citations for this publication.
As the demand for electric vehicles (EVs) continues to surge, improvements to energy management systems (EMS) prove essential for improving their efficiency, performance, and sustainability. This paper covers the distinctive challenges in designing EMS for a range of electric vehicles, such as electrically powered automobiles, split drive cars, and P-HEVs. It also covers significant achievements and proposed solutions to these issues. The powertrain concept for series, parallel, series-parallel, and complex hybrid electric cars was also disclosed in this study. Much of this analysis is dedicated to investigating the various control strategies used in EMS for various electric vehicle types, which include global-optimization approaches, fuzzy rule based, and real-time optimization-oriented strategies. The study thoroughly evaluates the strengths and shortcomings of various electric vehicle strategies, offering valuable insights into their practical implementation and effectiveness across different EV models, such as BEVs, HEVs, and PHEVs.
Bidirectional converters (BDCs) are important in electric vehicles (EVs). This study investigated different bidirectional power converters and their efficiencies, energy densities, and cost parameters. Control methods such as pulse width modulation (PWM) and hysteresis control are also examined. Comparisons on how well they regulate the output voltage and current in converters are performed. This paper also provides an overview of the types of BDCs used in EVs, including flyback, forward, and push-pull converters. The efficiency, energy density, and cost of each converter are assessed to identify the most appropriate model for EVs. The control methods including the advantages and disadvantages of PWM and hysteresis control and how they influence converter performance are examined. Suggestions for applying bidirectional power converters and control approaches in electric vehicles are also proposed. In summary, the paper consolidates the existing research, findings, and current gaps leading to advancements in the field and provide resources for emerging methodologies and concepts for advanced electric mobility.
To reduce pollution and energy consumption, particularly in the automotive industry, energy saving is the main concern, and hence, Electric vehicles (EVs) are getting significantly more attention than vehicles with internal combustion engines (IC engines). Electric motors used in Electric Vehicles (EVs) must have high efficiency for maximum utilization of the energy from the batteries or fuel cells. Also, these motors must be compact, lightweight, less expensive and very easily recycled. Further, to obtain better dynamic performance, various motor control strategies are used to control the speed of the motor. And to have increased reliability, sensorless speed control techniques that offer sufficiently high performance are used. The sensorless speed control techniques are largely divided into three groups: state observer methods, indirect measurement methods and saliency-based methods. Generally, the state observer uses back emf or flux linkage to estimate the speed of the motor. Since the back emf is directly proportional to the rotor speed, at low-speed back emf based method will give poor performance. The current-based Model Reference Adaptive System (MRAS) model is also popular for estimating low speed; however, assessments deteriorate during high performance applications such as EV. This paper presents an artificial neural network (ANN)-deployed sensorless speed control of permanent magnet synchronous motor (PMSM) drive used in EVs. In this paper, the estimation of speed using the current-based MRAS model is discussed and compared with the proposed ANN-based controller, which shows significant improvement in the performance of EV motor drives. The MATLAB simulation and experimental results are presented to validate the proposed algorithm.
In the power conversion system of electric vehicles (EVs), the double-input converter serves as a crucial component. Using a combination of battery and super-capacitor as energy storage in electric vehicles can greatly improve the vehicle's efficiency and prolong the battery's lifespan. The paper presents a novel non-isolated bidirectional dual input-single output (DISO) DC-DC converter. Its primary objective is to enable the efficient interfacing of battery and supercapacitor in electric vehicle applications. The proposed converter can perform bidirectional power control in both buck and boost modes. Compared to the conventional ones, the DISO converter has a simple structure, and it employs only five semiconductor switches, one inductor and one capacitor. The paper presents a comprehensive explanation of how the converter operates, its analysis and design, and an examination of various modes of power transfer that can be utilized. The simulation of the converter has been carried out on the MATLAB/Simulink platform.
Torque ripple is one of the most critical problems in PMSM system. In this paper, a neural network (NN) torque compensator is combined with a conventional extended state observer (ESO)-based active disturbance rejection controller (ADRC) system to suppress the torque ripple at wide machine operation speed range by generating the optimal current reference. The ESO is able to estimate and reject the low-frequency component in the torque ripple, while the remaining disturbances can be learned and compensated by the neural network. Compared with commonly used schemes, the proposed method does not need to analyze the influence of various sources of the torque ripple, such as the cogging torque, non-sinusoidal back-EMF, parameter variations, and unmodeled disturbances. In addition, the simple structure of the neural network helps reduce the computation time and save computer memory. The effectiveness of the proposed neural network compensator with both the rotor position and mechanical angular velocity as inputs is verified in the experiment under different operation speeds.
BLDC motor applications require precise position and speed measurements, traditionally obtained with sensors. This article presents a method for estimating those measurements without position sensors using terminal phase voltages with attenuated spurious, acquired with a FPGA that also operates a PWM-controlled inverter. Voltages are labelled with electrical and virtual rotor states using an encoder that provides training and testing data for two three-layer ANNs with perceptron-based cascade topology. The first ANN estimates the position from features of voltages with incremental timestamps, and the second ANN estimates the speed from features of position differentials considering timestamps in an acquisition window. Sensor-based training and sensorless testing at 125-1,500 rpm with a loaded 8-pole-pair motor obtained absolute errors of 0.8 electrical degrees and 22 rpm. Results conclude that the overall position estimation significantly improved conventional and advanced methods, and the speed estimation slightly improved conventional methods, but was worse than in advanced ones.
Environmental pollution is one of the serious issues in the current era. In diminishing this issue, electric vehicles (EVs) can play one key role. Again, along with the development of modern drive technologies in various electric traction applications, a comprehensive understanding of proper motor selection, is also becoming much important. This paper compares induction motor (IM), brushless DC (BLDC) motor and permanent magnet synchronous motor (PMSM) for EV traction applications. In this study, field-oriented control (FOC) strategy with ANN based controller, is chosen due to its accuracy, fast dynamical response and ease of proper torque and speed control. This comparative study and the model simulation has been carried out in the MATLAB/Simulink environment.
Permanent magnet synchronous motors (PMSM) have been accepted and used as variable speed drives because of its static and dynamic characteristics such as lesser volume, light weight, high efficiency, small inertia and hence torque to inertia ratio is high, torque to volume ratio is high, maintenance free and ease of control. PMSM are now days a good choice as high performance drives such as robotic industries and machine manufacturing tools. The proposed technique incorporating space vector pulse width modulated (SVPWM) inverter, for speed control of PMSM drive has been presented in this paper, which has advantage of fast dynamic response with low ripples in speed, torque response, and least dependency on motor parameters. The mathematical modeling of PMSM motor along with vector control strategy is presented in this paper which employs the SVPWM technique for generation of inverter output voltage. The simulation results carried out for the reference speed and reference torque command given by some user defined signal, the output nearly catches the input reference command. Due to requirement of the Voltage Source fed Inverter the PWM technique is employed as 3 level SVPWM strategy which is having maximum achievable peak fundamental phase voltage for conventional sinusoidal modulation. Simulation results show that it has good dynamic and static characteristics with lesser overshoot and small speed and ripple free torque pulsation. A simulation model is developed in MATLAB / SIMULINK to judge the various performance parameters.
This paper presents a new modular hybrid excited switched reluctance motor (MHESRM) with permanent magnets (PMs) embedded between stator teeth. The effect of the PMs is to reduce the magnetic saturation in the teeth of the stator, poles, and yoke, and to increase the magnetic flux in the air gap increases, leading to the significant enhancement of developed electromagnetic torque. Also, the magnetic equivalent circuit shows that when both groups of PMs are embedded in the structure, the air gap magnetic flux increases. MHESRM, with four teeth per stator's pole, has better static performance. However, MHESRM with two teeth per stator's pole has better dynamic performance and a higher mean steady-state electromagnetic torque. Finally, the 16/18 MHESRM opts due to its superior dynamic performance. The proposed MHESRM is optimized using the genetic algorithm for the best torque performance. Additionally, its dynamic and static characteristics are compared with an 8/12 SRM, an 8/4 SRM, three hybrid reluctance motors, and a flux-switching PM motor of the same size. The prototype of the proposed topology was built for experimental verification. The simulation and experimental results for the proposed motor agree significantly, leading to high torque density.
A simulation analysis of a voltage source inverter using Space Vector Pulse Width Modulation (SVPWM) to control the speed of a Permanent Magnet Synchronous Motor (PMSM) has been presented in this research. PMSM is an AC drive that widely helps in high power propulsion systems. SVPWM is one of the best PWM techniques. The SVPWM is powered by a voltage source inverter connected to the PMSM. Space vector modulation is responsible for developing pulses to control the switches of the inverter. This research introduces an SVPWM-fed three-phase voltage source inverter for controlling the speed of PMSM drives using MATLAB/Simulink.
This paper proposes a methodology to increase the lifetime of the central battery energy storage system (CBESS) in an islanded building-level DC microgrid (MG) and enhance the voltage quality of the system by employing the supercapacitor (SC) of electric vehicles (EVs) that utilize battery-SC hybrid energy storage systems. To this end, an adaptive filtration-based (FB) current-sharing strategy is proposed in the voltage feedback control loop of the MG that smooths the CBESS current to increase its lifetime by allocating a portion of the high-frequency current variations to the EV charger. The bandwidth of this filter is adjusted using a data-driven algorithm to guarantee that only the EV's SC absorbs the high-frequency current variations, thereby enabling the EV's battery energy storage system (BESS) to follow its standard constant current-constant voltage (CC-CV) charging profile. Therefore, the EV's SC can coordinate with the CBESS without impacting the charging profile of the EV's BESS. Also, a small-signal stability analysis is provided indicating that the proposed approach improves the marginal voltage stability of the DC MG leading to better transient response and higher voltage quality. Finally, the performance of the proposed EV charging is validated using MATLAB/Simulink and hardware-in-the-loop (HIL) testing.
A second-order ordinary differential equation model is originally constructed for the phase q current system of a permanent magnet synchronous motor (PMSM). The phase q current model contains the effect of a counter electromotive force (CEMF), which introduces nonlinearity to the system. In order to compensate the nonlinearity and system uncertainties, a traditional sliding mode controller (SMC) combined with a low-pass filter (also known as a modified SMC) is designed on the phase q current model. The low-pass filter overcomes chattering effects in control efforts, and hence improves the performance of the controller. The phase q current control system is proved to be stable using Lyapunov approach. In addition, an alternative active disturbance rejection controller (ADRC) with a reduced-order extended state observer (ESO) is applied to control the speed output of PMSM. Both SMC and ADRC are simulated on the PMSM system. The simulation results demonstrate the effectiveness of these two controllers in successfully driving the current and speed outputs to desired values despite load disturbances and system uncertainties.
The main challenge of PMSM implementation in the adjustable-speed drives especially in automotive industry is to attain the optimal PMSM drive performance. Vector control is proved to be the best method in controlling synchronous machine such as PMSM. This paper objective is to design a speed control system for the manufactured inset rotor PMSM, which integrates the interleaved DC-DC boost converter, inverter, and sinusoidal pulse width modulation and fed by the battery bank DC source. The proposed speed control in this paper employs FOC vector control technique with PI controller which control both converter and inverter independently. This paper investigates the effectiveness of the proposed speed control method for driving the manufactured inset rotor PMSM. To verify the effectiveness of the designed speed control system,computer simulation is conducted. The motor performances are observed in operating condition with disturbance in form of sudden change of load torque. The simulation results show that the control method is stable but the rotor speed still affected by the given disturbance.
In the field of Permanent Magnet Synchronous Motor (PMSM) control, SVPWM method is one of the new techniques have been developed in recent years. This paper presented the SVPWM method, including the reference voltage projection and the space vector duration calculation. Then the simulation results also are presented.
This paper presents a novel approach to the field-oriented control
(FOC) of induction motor drives. It discusses the introduction of
artificial neural networks (ANNs) for decoupling control of induction
motors using FOC principles. Two ANNs are presented for direct and
indirect FOC applications. The first performs an estimation of the
stator flux for direct field orientation, and the second is trained to
map the nonlinear behavior of a rotor-flux decoupling controller. A
decoupling controller and flux estimator were implemented upon these
ANNs using the MATLAB/SIMULINK neural-network toolbox. The data for
training are obtained from a computer simulation of the system and
experimental measurements. The methodology used to train the networks
with the backpropagation learning process is presented. Simulation
results reveal some very interesting features and show that the networks
have good potential for use as an alternative to the conventional
field-oriented decoupling control of induction motors