Performance Improvement of Field oriented Controlled Permanent Magnet Syn-chronous Motor Based Electric Vehicle Drives by ANN Controller

Abstract

The present work represents the design and testing of artificial neural network controllers for permanent magnet synchronous motor drives applied in electric vehicles along with the conventional controller. This control methodology in-tends to leverage the power of adaptive learning offered by artificial neural networks (ANN) to overcome the challenges of electric vehicle propulsion, such as varied operating conditions, time-varying parameters, and nonlinear dynamics of systems. Simulation studies indicated that ANN controllers outperformed conventional PI-based field-oriented control methods for all the aspects of speed regulation, ripple reduction in torque, and robustness against perturbation. Real-time modifications of the neural network model to changes in load and outside noise improve performance without the need for complicated decoupl-ing or tedious tuning. These results demonstrate that the ANN technique for the control of PMSMs is quite suitable for elec-tric vehicles and easily integrated into them. It can help in driving by improving efficiency, making runs smoother, and could adapt to various needs of multimodal transportation with further improvements.