SENSORLESS VECTOR CONTROL OF AN ELECTRIC VEHICLE INDUCTION MOTOR WITH A FUZZY ALGORITHM FOR SPEED OBSERVER ADAPTATION
DOI:
https://doi.org/10.35546/kntu2078-4481.2024.1.15Keywords:
electric vehicle, induction motor, sensorless drive, vector control, speed observer, fuzzy algorithmAbstract
Due to their high reliability and relatively low cost, induction motors (IMs) are widely used in electric vehicles. In modern advanced systems of sensorless vector control of AC motors, instead of the measured rotor speed, its estimate is used, which is obtained with the help of a speed observer (SO). The advantages of sensorless control are better noise immunity, higher reliability, and lower cost of the IM automatic control system (ACS). An important feature of sensorless IM ACS is that in order to improve its dynamics, it is necessary to improve the dynamics of the SB, which is determined by the algorithm of the adaptation mechanism. When using the traditional proportional-integral (PI) algorithm for the adaptation of the SO, an increase in system performance may be accompanied by an undesirable increase in the oscillation of transients, which leads to an increase in energy losses in the IM. Therefore, the task of improving the SO adaptation algorithm in the IM ACS of an electric vehicle is currently important and relevant. The aim of this work is to build and study a vector control system for sensorless electric vehicle IM using a fuzzy PI algorithm for adaptation of the SO. In order to improve the properties of the sensorless vector control system for the IM of an electric vehicle, it is proposed to use its fuzzy version, the fuzzy -PI (FPI) algorithm, instead of the traditional PI algorithm, in the speed observer adaptation block. The fuzzy-logic block of the FPI algorithm with two input and one output linguistic variables is synthesized. Each linguistic variable corresponded to seven terms, among which five had the shape of a triangle and two had the shape of a trapezoid. The results of mathematical modelling have shown that the use of the FPI adaptation algorithm of the SO can significantly improve the dynamic and energy characteristics of the IM ACS. In the transient processes of compensating for changes in motor load, the dynamic speed deviation and control time are reduced by 12% and 22%, respectively. Energy losses in transients during stepwise speed control and load application are reduced by 13.3% and 9.8%, respectively.
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