Investigation of Intelligent Deep Convolution Neural Network for DC-DC Converters Faults Detection in Electric Vehicles Applications

Abstract

The fault investigation in DC-DC converters (DCCs) becoming necessity to provide consistent and robust electricity in electric vehicles (EVs) applications. Any kind of fault in DCCs leads to impacts the whole system. Therefore, it is essential to increase the robustness and reliability of DCCs. This paper investigates faults in DCCs for EV applications based on an intelligent deep convolution neural network (DCNN). The data obtained during the fault condition and the normal condition is provided to the intelligent-based system and the comparison produces the desired result. The simulation results demonstrate that the DCNN technique recommended in this study can rapidly and precisely detect and identify faults The MATLAB-21 simulation is used to detect the data of fault and normal conditions the intelligent-based deep neural network is used to detect the fault. Further, compared with PID and FLC controllers, the proposed DCNN technique gets state-of-the-art for fault detection results and can be beneficial for the prospect of innovative EV applications.