Performance analysis of DTC-SVM in a complete traction motor control mechanism for a battery electric vehicle

Klerk, Matthew Liam De; Saha, Akshay Kumar

doi:10.1016/j.heliyon.2022.e09265

Cited by 17 publications

(8 citation statements)

References 36 publications

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“…The current is directly determined, whereas the voltage is contingent upon the switching state ( and ) determined by the switching table. The transformation of these parameters into coordinates is conducted via the Concordia transformation, as follows 5 : The following equations are used to estimate the flux in the stator ( ) and the electromagnetic torque ( ) 24 : The following are the elements of stator flux: The formula that follows is employed to determine the stator flux position: Subsequently, the compared values of the estimated flux ( ) and electromagnetic torque ( ) with their corresponding reference ( and ) serve as hysteresis comparators’ inputs. Choosing the suitable voltage vector is determined by the control table.…”

Section: Fundamental Theoretical Formulationmentioning

confidence: 99%

“…Utilizing hysteresis controllers leads to significant ripples in flux and electromagnetic torque, as a result, it leads to current distortions and variable switching frequencies, which have the potential to compromise the quality of the output power and adversely affect the overall performance of the motor. Furthermore, the commonly employed approach in the DTC for speed control involves the use of a PI controller to eliminate static error and minimize response time, however, The speed typically exhibits an initial overshoot during startup and it’s influenced by the machine’s parameters 5 , 27 . Within this framework, to address the drawbacks of the traditional DTC a new control scheme is proposed and will be elaborated in the following section.…”

Section: Fundamental Theoretical Formulationmentioning

confidence: 99%

“…However, these numerous benefits come with challenges as the nature of the machine’s dynamic behavior is frequently intricate because its modeling yields a set of nonlinear, tightly coupled, and multivariable equations. Moreover, certain state variables, like flux, are not directly measurable 5 . These limitations call for the implementation of advanced control algorithms to effectively manage the real-time control of torque and flux in these machines.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation

Kasri,

Ouari,

Belkhier

et al. 2024

Sci Rep

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Enhancing the efficiency of the electric vehicle’s powertrain becomes a crucial focus, wherein the control system for the traction motor plays a significant role. This paper presents a novel electric vehicle traction motor control system based on a robust predictive direct torque control approach, an improved version of the conventional DTC, where the traditional switching table and the hysteresis regulators are substituted with a predictive block based on an optimization algorithm. Additionally, a robust predictive speed loop regulator is employed instead of the proportional-integral regulator, which integrates a new cost function with a finite horizon, incorporating integral action into the control law based on a Taylor series expansion. This technique’s primary benefit is its independence from the necessity to measure and observe external disturbances, as well as uncertainties related to parameters. The effectiveness of the suggested system was confirmed through simulation and experimental results under the OPAL-RT platform. The findings indicate that the proposed approach outperforms the conventional method in terms of rejecting disturbances, exhibiting robustness to variations in parameters, and minimizing torque ripple.

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Section: Fundamental Theoretical Formulationmentioning

confidence: 99%

Section: Fundamental Theoretical Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation

Kasri,

Ouari,

Belkhier

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Compared with ANN methods, the SVM model has higher accuracy and shorter computational time, with a maximum error of 5% in battery states estimation. Some researchers have also used a combination of SVM and other methods to estimate battery states [61][62][63][64][65].…”

Section: ) Svm Modelmentioning

confidence: 99%

Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

Zhao

Zhou

et al. 2023

WEVJ

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Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods.

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“…Then, the comparison results can procreate the torque and the flux errors, respectively. These errors are afterwards transferred to two hysteresis regulators to define the suitable bands (De Klerk and Saha, 2022). For the flux controller [Figure 2(a)], the instantaneous error of stator flux is defined as the difference between the stator flux reference and its instantaneous value (Δ φ = φ sref − φ s ).…”

Section: Direct Torque Control Of Induction Motor Drivementioning

confidence: 99%

Sensorless DTC-SVM applied to an induction motor controlled by a three-level inverter using SMSFO

Jnayah

Khedher

2022

COMPEL

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Purpose The direct torque control (DTC) of induction motor (IM) drive is featured by high ripples in the electromagnetic torque and stator flux profiles because they are controlled by two hysteresis regulators. Furthermore, the machine flux is not directly measurable. Hence, it is better to reconstitute it from the instantaneous electrical equations of the machine. Once the stator flux is estimated, we can guarantee a reliable sensorless DTC control. Thus, the purpose of this research work is to ensure fast response and full reference tracking of the IM under sensorless DTC strategy with desired dynamic behavior and low ripple levels. Design/methodology/approach In this work, an improved DTC strategy, which is DTC_SVM_3L, is suggested. The first step of the designed approach is to substitute the conventional inverter feeding the motor with a three-level inverter because it guarantees reduced switching losses, improved quality of voltage waveform and low-current total harmonic distortion rate. The second aim of this paper is to make the IM operate at a constant switching frequency by using the nearest three vectors-based space vector modulation (SVM) technique rather than hysteresis controllers. The third objective of this study is to conceive a sliding-mode stator flux observer, which can improve the control performances by using a sensorless algorithm to get an accurate estimation, and consequently, increase the reliability of the system and decrease the cost of using sensors. The stability of the proposed observer is demonstrated based on the Lyapunov theory. To overcome the load change disturbance in the proposed DTC control strategy, this paper exhibits a comparative assessment of four speed regulation methods: classical proportional and integral (PI) regulator, fuzzy logic PI controller, particle swarm optimization PI controller and backstepping regulator. The entire control algorithm was tested under different disturbances such as stator resistance and load torque variations. Findings It was ascertained that the IM, controlled with three-level inverter, exhibits good performances under the proposed DTC-SVM strategy based on a sliding-mode observer. The robustness of the suggested approach against parameter variations is also proved. Originality/value The theoretical development of the proposed control strategy is thoroughly described. Then, simulations using Matlab/Simulink software are launched to investigate the merits of the sensorless DTC-SVM command of three-level inverter-fed IM drive with different speed regulators.

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Performance analysis of DTC-SVM in a complete traction motor control mechanism for a battery electric vehicle

Cited by 17 publications

References 36 publications

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation

Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

Sensorless DTC-SVM applied to an induction motor controlled by a three-level inverter using SMSFO

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