Usage of ANN(IOT) in PMBLDC motor controlled by Zeta Converter for Electric Vehicle Application

In the latest scenario of automated industries, permanent magnet brushless DC (PMBLDC) motors are known to have marvelous scope of industrial applications. PMBLDC is widely used because of its design methodology, peak power density, efficiency with starting torque known to be high, reliable operational span, and extended speed ranges, motors are often used in fields that need high fidelity and specialized control. This research examines the use of Artificial Neural Networks (ANN) to regulate a Permanent Magnet Brushless motor (PMBLDC) controlled by a zeta converter in the application of an electric vehicle. The key advantage of the Zeta converter is that it drives the Permanent Magnet Brushless Direct Current Motor and control it speed without suffering from polarity reversal, and aids in better charging of batteries in electric vehicles. A battery is a primary or secondary bidirectional power source in electric vehicles. The power sourced or sinked by the battery is used to optimise the energy management of the vehicle. The battery's power is determined by a number of factors like current, charge state, ambient temperature. The Neural Network controller has proven to be an effective one for high-performance speed control and helps in optimizing with low power applications. The artificial neural network was trained using data acquired from the zeta converter at various speeds, voltages, and frequencies. The trained neural networks could perform consistently without the use of a mechanical speed sensor, making it an appropriate technique for driving an electric vehicle.