Simulation modeling of PMBLDC motor speed control in electric propulsion of EV system using adaptive nonlinear fuzzy sliding mode controller (ANF-SMC)

Abstract

In the current scenario of transportation system, vehicles of conventional are replaced with Electric Vehicles (EVs) with Permanent Magnet Brushless Direct Control Motors (PMBLDCMs) because of the many advantages of the usage of such motors in EVs. Despite the many advantages of the usage of EVs with PMBLDCMs, there were some disadvantages, among them were speed control and dealing with uncertainty problems in the usage of PMBLDCMs in EVs. In rectifying the above-mentioned problems, this work proposed and implemented an ideology of using the nonlinear controller strategy Adaptive Neuro-Fuzzy Sliding Mode Control (ANF-SMC) in the Electric Propulsion Subsystem (EPSS) of EVs with the usage of the proposed nonlinear controller under not loaded and fully loaded vehicles with constant, variable and sudden disturbance in speed conditions. Simulation was carried out using MATLAB version R2020b with Fuzzy Tool Kit (FTK) and simulation performance comparison was done with the existing four (one linear and three nonlinear) controllers. Comparative simulation results infer that the proposed nonlinear controller performs well with zero overshoot and less settling time than that of the existing four (one linear and three nonlinear controllers).