Energy Auditing in Three-Phase Brushless DC Motor Drive Output for Electrical Vehicle Communication Using Machine Learning Technique

Abstract

Using predictive nonlinear optimal control, this model examines the output power of a three-phase brushless DC motor (BLDC) drive to ensure that it is stabilized (PNOC). A BLDC is a kind of electric motor that is used in a variety of applications and is one of the models of electric motors that are utilized in constant speed applications. In this motor, the movable component of the rotor created torque and the rotor rotated in a position of low reluctance; the location of the rotor is determined by the motor’s maximum inductance value. The BLDC drive controls the motor via the converter circuit, and the converter circuit ensures that the motor receives the appropriate output power. The project manager should have a thorough discussion with the team about the demagnetization of the malfunctioning BLDC motor before beginning this job. It is possible to model a machine using many existing technologies, such as electrical equivalent circuit diagram (EEC), which are based on a number of assumptions that make the analysis process or the analysis approach simpler. Despite numerical methodologies, these approach scenarios give frequency domain loop (FDL) precision frequency domain, using a suitable weight strategy to deliver high power solution creation (NM). The purpose of this essay is to integrate these two technologies in order to make contributions via the development of a new hybrid EEC-FDL model closed-loop brushless DC motor. PNOC is a driving system that uses predictive nonlinear optimal control (PNOC). The generated model is subjected to simulations under both healthy and incorrect settings, respectively. MATLAB software is utilized to construct the simulation of the control circuit, and simulation outputs are validated by experimental findings. Predictive nonlinear optimal control (PNOC) is employed to eliminate torque ripple and improve system stability.