Abstract

Electric Vehicles (EVs) are anticipated to dominate passenger car transportation, playing a pivotal role in advancing sustainable mobility. However, with the increasing enthusiasm for EVs, impediments endure within the realm of power transmission. This is especially evident in addressing challenges related to minimizing torque ripple and implementing advanced control techniques in traction for high-performance and efficient operation of EVs. Numerous control algorithms for motor drives have been developed in the recent past but face challenges in attaining effective control under varying drive cycles of EVs. To tackle these challenges, motor drive control algorithms integrate various control techniques, including field orientation control, model predictive control, intelligent control, etc. This paper proposes an innovative online-tuned MPCC algorithm based on the adaptive neuro-fuzzy inference system (ANFIS). The traditional proportional–integral (PI) controller is replaced with an adapted ANFIS algorithm, and the tuning of ANFIS parameters is achieved by leveraging the error between the reference and adjustable models through a hybrid training algorithm. The proposed novel control technique improves the dynamic speed response of permanent magnet synchronous motor drives EVs. This improvement is realized by replacing the PI-HCC controller with an ANFIS controller coupled with MPCC. A laboratory prototype of the proposed control technique for EVs has been developed, and a comparative analysis of ANFIS-MPCC techniques with other known control techniques has been presented. This paper also demonstrates the importance of choosing optimal motor control techniques for torque ripple minimization and improving the overall performance of EVs.

中文翻译：

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一种改进的 AFIS 模型预测电流控制方法，可最大限度地减少 PMSM 驱动的电动汽车中的扭矩和电流纹波

摘要

电动汽车 (EV) 预计将主导客车运输，在推动可持续交通方面发挥关键作用。然而，随着人们对电动汽车的热情日益高涨，电力传输领域仍然存在障碍。这在解决与最小化扭矩脉动和在牵引中实施先进控制技术以实现电动汽车高性能和高效运行相关的挑战时尤其明显。近年来，已经开发了许多用于电机驱动的控制算法，但在电动汽车的不同驱动周期下实现有效控制面临着挑战。为了应对这些挑战，电机驱动控制算法集成了各种控制技术，包括磁场定向控制、模型预测控制、智能控制等。本文提出了一种基于自适应神经模糊推理系统（ANFIS）的创新在线调谐MPCC算法。传统的比例积分（PI）控制器被自适应ANFIS算法取代，并通过混合训练算法利用参考模型和可调模型之间的误差来实现ANFIS参数的调整。所提出的新颖控制技术提高了永磁同步电机驱动电动汽车的动态速度响应。这一改进是通过将 PI-HCC 控制器替换为与 MPCC 结合的 AFIS 控制器来实现的。所提出的电动汽车控制技术的实验室原型已经开发出来，并对 ANFIS-MPCC 技术与其他已知控制技术进行了比较分析。本文还论证了选择最佳电机控制技术以最小化扭矩纹波和提高电动汽车整体性能的重要性。