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Sensorless DTC IM drive for an EV propulsion system using a neural network

机译：使用神经网络的电动汽车推进系统无传感器DTC IM驱动器

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摘要

This paper deals with a sensorless direct torque control (DTC) of an induction motor (IM) for an electric vehicle (EV) propulsion system using a neural network. The drawback of conventional DTC is the generation of relatively large torque ripple. In the proposed scheme, first the traditional switching lookup table of a three-level torque controller DTC is replaced with a neural network controller. For further reducing the torque ripples, a three-level torque controller is replaced by the five-level torque controller and then the switching lookup table of the five-level torque controller DTC is replaced with a neural network controller. These sensorless DTC schemes of an IM drive are simulated using Matlab/Simulink. The simulated results are compared with conventional DTC and it is found that the ripples in the torque, as well as in stator current, are reduced drastically.
机译：本文研究了使用神经网络的电动汽车（EV）推进系统的感应电动机（IM）的无传感器直接转矩控制（DTC）。传统DTC的缺点是会产生较大的转矩波动。在提出的方案中，首先将三级转矩控制器DTC的传统开关查找表替换为神经网络控制器。为了进一步减小转矩波动，将三级转矩控制器替换为五级转矩控制器，然后将五级转矩控制器DTC的开关查找表替换为神经网络控制器。使用Matlab / Simulink对IM驱动器的这些无传感器DTC方案进行了仿真。将模拟结果与常规DTC进行比较，发现转矩和定子电流中的纹波都大大减少了。

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