International Research Journal of Education and Technology

The electric vehicles (EVs) have ushered in a new era of sustainable transportation. However, ensuring the safety and reliability of these vehicles is of paramount importance. One critical aspect is the detection and tracking of faults in the interior wiring systems, which are responsible for various vital functions within the EV. This paper presents an innovative approach to fault tracking within EV interior wiring, combining the power of MATLAB with Augmented Reality (AR) technology. MATLAB serves as the computational backbone of the proposed system, offering robust algorithms for data analysis and signal processing. The system employs a network of sensors strategically placed throughout the EV's interior to monitor electrical circuits continuously. In the event of a fault, MATLAB processes sensor data to pinpoint the location and nature of the fault. This information is then seamlessly integrated into an AR interface. Augmented Reality is harnessed to provide real-time visual cues to service technicians, enhancing fault diagnosis and repair procedures. Through AR glasses or a mobile device, technicians can visualize the EV's interior in real-time and receive intuitive visual overlays highlighting the precise location of the fault, along with step-by-step repair instructions. This integration of AR not only expedites the fault-tracking process but also reduces the margin for error, ultimately leading to safer and more efficient maintenance. In conclusion, the fusion of MATLAB analytical capabilities with Augmented Reality technology offers a novel and effective solution for fault tracking within electric vehicles' interior wiring systems. This approach not only enhances the safety and reliability of EVs but also represents a significant step forward in the evolution of automotive maintenance and repair practices, a valuable tool in the electric vehicle industry. Keywords: MATLAB, Augmented Reality, AR glasses, mobile device, Electric Vehicle, wiring Harness, data analysis, fault diagnosis, repair procedures