Minimum Copper Loss Direct Torque Control of Brushless DC Motor Drive in Electric and Hybrid Electric Vehicles

Zhang, Qingchao; Deng, Junjun; Fu, Na

doi:10.1109/access.2019.2927416

Cited by 13 publications

(8 citation statements)

References 15 publications

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“…In the literature [24], generalized predictive fractional order PI control was combined with lion swarm optimization to enable the optimal signal to come into the system, and an experimental platform was built to verify the feasibility. A new copper loss minimizing DTC was proposed in the literature [25] to achieve BLDCM in DTC while achieving high efciency and high torque fne reading, using a three-phase conduction voltage vector meter to achieve tracking control of the magnetic chain, cutting out the torque pulsation caused by the back EMF and nonideal phase change in the conventional two-phase conduction voltage vector meter DTC, and improving control accuracy. Cao et al [26] produced a refection on the BLDCM braking case and proposed a smooth torque control strategy for the BLDCM that corresponds to the direct torque control performance in diferent speed ranges according to diferent modulation methods to achieve controllability of the braking torque and also reduce the phase change torque.…”

Section: A Brief Summary Of Recent Research Reportsmentioning

confidence: 99%

Optimized Model Torque Prediction Control Strategy for BLDCM Torque Error and Speed Error Reduction System

Yuan,

Liu,

Chen

et al. 2023

Journal of Electrical and Computer Engineering

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This paper presents an improved whale optimization algorithm (IWOA) for optimizing the model predictive torque control (MPTC) of brushless DC motor (BLDCM) to further reduce the problems of strong torque pulsation and high ripple caused by the special structure of BLDCM. IWOA adds a randomized convergence factor strategy to the original algorithm, enabling the parameter weights to be adjusted in time. The relative error between the training set and the predicted values is reduced, and a suitable interval is selected for the target. The proposed method takes into account the switching frequency loss factor in the MPTC system of BLDCM, discarding the traditional trial-and-error method and choosing to control the parameter adjustment by the degree of deviation. The IWOA is compared with the popular whale optimization algorithm (WOA), dragonfly algorithm (DA), ant colony optimization (ACO) algorithm, and grey wolf optimization (GWO) algorithm on the MATLAB SIMULINK platform to verify the effectiveness of the method in dealing with improved chain tracking, reduced torque pulsation, and reduced speed error. The simulation results show that IWOA performs well, with an efficiency of 94.32%.

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Section: A Brief Summary Of Recent Research Reportsmentioning

confidence: 99%

Optimized Model Torque Prediction Control Strategy for BLDCM Torque Error and Speed Error Reduction System

Yuan,

Liu,

Chen

et al. 2023

Journal of Electrical and Computer Engineering

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“…However, when the perturbations are presented, as in (15), the control law (16) lacks of robustness. Therefore, in this paper, to add robustness to the control law (16), the integral sliding modes is proposed, to develop the Passivity with Sliding Modes (PSM) technique.…”

Section: Passivity With Sliding Modes Controlmentioning

confidence: 99%

“…Finally, the first part of the control input (18), u 0 , in (25) is chosen as a classical passive output feedback, as in (16). Now, the following result can be established: Theorem 1: If the Assumptions H.1 -H.3 holds, the solution of the closed-loop system (15) with (18) tends asymptotically to its steady-state value.…”

Section: Passivity With Sliding Modes Controlmentioning

confidence: 99%

“…Gain scheduling technique has been applied in electromechanical systems, however, the tuning could be difficult due to it is necessary to adjust several parameters [11]. Furthermore, nonlinear control techniques could be applied to the BLDC motor, including adaptive control [12], intelligent control [13], [14], backstepping [15] and other techniques like torque control [16]. However, the controllers presented were implemented in several applications, different from the spincoater machines, which need different requirements and are subject to different conditions.…”

Section: Introductionmentioning

confidence: 99%

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Spin-Coater Machine Control Via Passivity With Sliding Modes

Huerta

Vázquez

2020

IEEE Access

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In this paper, a novel control scheme for a spin-coater machine is presented, based on Passivity and Sliding Modes. The machine is used to make semiconductor thin films with a centrifugation force. A brushless direct current motor was selected as an actuator, its mathematical model is presented, including the mechanical and electrical dynamics. The control objective is the rotor speed regulation, with maximum acceleration. Taking advantage of the energy properties of the system, a sliding manifold is selected and, in order to reject the matched perturbations, the Integral Sliding Modes technique is applied. Then, with the unperturbed system, a passivity feedback is applied, to guarantee the stability around the desired equilibrium point. To complete the control scheme, an observer is included to estimate the unmeasurable estates. To prove the effectiveness of the proposed control scheme, it was implemented in a spin-coater machine and compared with a gain scheduled PID. The experimental results show a good performance of the closed-loop system, in spite of perturbations. INDEX TERMS Brushless motors, nonlinear control systems, sliding mode control, spinning machines.

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“…Besides, there are some other constraints of two-wheeler EVs, such as limited energy storage of battery pack and their effective utilization, limited speed, limited range, high initial cost and the proper choice of electric motor [1,2]. EVs utilize different types of the motor such as Induction Motor (IM) [3], Switched Reluctance Motor (SRM) [4], DC motor [5], Permanent Magnet Synchronous Motor (PMSM) [6], BLDC [7] etc. Among all these motors, IM and BLDC drive for EVs are widely used in a commercial vehicle.…”

Section: Introductionmentioning

confidence: 99%

Optimization of BLDC-based electric vehicles: Vehicle dynamics modelling through dual-motor approach and designing a novel augmented TLBO algorithm for PID control

Kumar,

Bera,

Kumar

2024

Eng. Res. Express

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Sample The work primarily focuses on increasing the efficiency of EV drive in electric two-wheeler by working on several aspects, such as modulating the vehicle's design, optimizing the control strategy, and increasing the speed range using a dual-motor approach. The dynamics of electric two-wheeler have been discussed with a mathematical vehicle model and further tuning of several aspects. Besides, this paper also introduces a novel Augmented Teaching and Learning based Optimization (ATLBO) technique designed exclusively to control BLDC motors for the electric two-wheeler vehicle. Besides, the designed technique has been implemented for the widely used commercial e-bike of Hero Company. Therefore, an analysis has been performed to increase the vehicle's speed range using a dual motor, from 45 km/hr to 62 km/hr, proving to be a viable alternative to a single motor generally used in an electric bike. ATLBO technique has been designed against a conventional TLBO to optimize the proportional-integral-derivative (PID) controller for the speed control of a linear brushless DC (BLDC) motor. Furthermore, the literature has validated the merits of the presented novel control technique. The only disadvantage of using a dual motor is the initial cost, but the overall cost is moderated in the long-term usage for its augmented performance parameters. The performance parameters of the above technique are analyzed against other optimization techniques like conventional Teaching and Learning based optimization (TLBO), Particle Swarm Optimization (PSO). MATLAB/Simulink models the brushless DC motor and implements ATLBO, TLBO, and PSO algorithms. It has been found that the response obtained from ATLBO is comparatively much faster than other optimization techniques, which supports the motor for quick acceleration as well as more efficient in improving the step response characteristics such as rise time, settling time, and steady-state error in the speed control of a linear BLDC motor.

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Minimum Copper Loss Direct Torque Control of Brushless DC Motor Drive in Electric and Hybrid Electric Vehicles

Cited by 13 publications

References 15 publications

Optimized Model Torque Prediction Control Strategy for BLDCM Torque Error and Speed Error Reduction System

Optimized Model Torque Prediction Control Strategy for BLDCM Torque Error and Speed Error Reduction System

Spin-Coater Machine Control Via Passivity With Sliding Modes

Optimization of BLDC-based electric vehicles: Vehicle dynamics modelling through dual-motor approach and designing a novel augmented TLBO algorithm for PID control

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