Battery Management for Electric Vehicle Using Low Voltage DC-DC Converter

Electric Vehicles are a predominant technology to achieve sustainable growth in the future. The speed control in the electrical vehicle application is still in processing to make the operation more efficient. This paper exhibits the efficiency level of the energy management of current and voltage controls in the low voltage DC-DC converter which uses solar energy as the charge which is the input source. The proportional integral derivative power controller is tuned up for a perfect BLDC dynamic motor model compatible with a perfect balance between robustness value and faster response. The control systems and the automation evolution is accelerated which uses the KNN algorithm of the machine learning and fuzzy technique methods. It also depicts the comparative study on best effectiveness and good performance between either proportional machine learning or fuzzy controller methods which are designed extensively using the MATLAB. The measurements of the current and voltage readings are displayed using cloud technology. The result analysis is done critically by studying the responses for both controllers from the plotted graph analysis. The administered techniques used here help to increase the efficiency of the Brushless DC motor which demonstrates the evolutionary growth applications of electric vehicle systems.