An Improved Model Predictive Current Control of BLDC Motor With a Novel Adaptive Extended Kalman Filter–Based Back EMF Estimator and a New Commutation Duration Approach for Electrical Vehicle

IF 1.8 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Remzi Inan
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Abstract

As a result of the increasing use of electric vehicles, ensuring high-performance speed and torque control of brushless direct current (BLDC) motors has become of great importance for energy efficiency. In order to prevent the torque ripple of the finite control set model predictive current control (FCS-MPCC), commutation moments are detected by Hall effect sensors in conventional methods. However, this method cannot exhibit a long-life structure because of physical strain damaging the sensors and electrical connections. In this study, commutation moments and durations are captured and determined with a new approach. Commutation moments are captured with zero crossing detectors and commutation durations are determined by using the position information obtained from the encoder. Moreover, three-phase back electromotive forces (EMFs) of the BLDC motor applied to FCS-MPCC to predict the stator phase currents are estimated with a novel adaptive extended Kalman filter (AEKF) which has the estimation capability without any speed sensor. Furthermore, another improvement is implemented in the calculation of the cost function of FCS-MPCC by taking into account the difference between the predicted and the reference torque of the BLDC motor different from the conventional MPCC methods. The proposed drive system is tested under different scenarios at various speeds under load torque, stator resistance, and leakage inductance variations in simulation. It is proven by simulation results that phase commutations can be achieved stably with the proposed phase commutation determination method. In addition, the simulation results show that the proposed novel AEKF estimator and the FCS-MPCC in which the cost function is calculated by regarding not only the current error but also the moment error have impressive prediction and control performance, respectively.

Abstract Image

基于自适应扩展卡尔曼滤波的新型反电动势估计器和电动汽车换相持续时间方法改进无刷直流电机模型预测电流控制
随着电动汽车使用量的不断增加,确保无刷直流电机的高性能转速和转矩控制对于提高能效具有重要意义。为了防止有限控制集模型预测电流控制(FCS-MPCC)的转矩脉动,传统方法采用霍尔效应传感器检测换相矩。然而,这种方法不能表现出长寿命的结构,因为物理应变破坏传感器和电气连接。在本研究中,用一种新的方法捕获和确定换相时刻和持续时间。换相矩由零交叉检测器捕获,换相持续时间由编码器提供的位置信息确定。此外,采用一种新型的自适应扩展卡尔曼滤波器(AEKF)估计无刷直流电动机的三相反电动势(emf),用于FCS-MPCC定子相电流的预测,该滤波器不需要任何速度传感器即可估计定子相电流。此外,本文还对FCS-MPCC成本函数的计算进行了改进,考虑了无刷直流电机的预测转矩与参考转矩之间的差异,与传统的MPCC方法不同。在负载转矩、定子电阻和漏感变化的情况下,仿真测试了所提出的驱动系统在不同速度下的不同场景。仿真结果表明,所提出的相换相确定方法可以稳定地实现相换相。此外,仿真结果表明,所提出的AEKF估计器和同时考虑电流误差和矩误差计算代价函数的FCS-MPCC分别具有良好的预测和控制性能。
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来源期刊
International Journal of Circuit Theory and Applications
International Journal of Circuit Theory and Applications 工程技术-工程:电子与电气
CiteScore
3.60
自引率
34.80%
发文量
277
审稿时长
4.5 months
期刊介绍: The scope of the Journal comprises all aspects of the theory and design of analog and digital circuits together with the application of the ideas and techniques of circuit theory in other fields of science and engineering. Examples of the areas covered include: Fundamental Circuit Theory together with its mathematical and computational aspects; Circuit modeling of devices; Synthesis and design of filters and active circuits; Neural networks; Nonlinear and chaotic circuits; Signal processing and VLSI; Distributed, switched and digital circuits; Power electronics; Solid state devices. Contributions to CAD and simulation are welcome.
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