Advanced hardware with AI driven software to monitor Inverters and motor to detect trends and early failure causes to enhance safety for electric vehicles (xEV)

Abstract

Abstract: The integration of Artificial Intelligence (AI) with advanced hardware systems provides a novel approach to the real-time monitoring and predictive maintenance of power inverters and electric motors in electric vehicles (xEVs). In this study, we propose a comprehensive framework combining AI-driven software with hardware sensors to monitor the health and performance of inverters and motors, aiming to detect early signs of wear and failure. This proactive monitoring system is designed to analyze data trends related to voltage, current, temperature, and vibrations, which are crucial indicators of the operating conditions and longevity of powertrain components. Through the use of machine learning algorithms, the system learns and identifies abnormal patterns in these signals, helping to predict potential failures before they occur. Our solution focuses on optimizing the efficiency and safety of xEVs by preventing unanticipated breakdowns, which could cause significant operational disruptions or safety hazards. The AI model uses supervised learning to recognize patterns of normal behavior and anomaly detection to flag any deviations, providing an early warning for preventive action. The system’s ability to analyze real-time data from hardware sensors offers significant improvements over traditional diagnostic methods, which often rely on periodic checks or delayed responses to failures. The implementation of such AI-powered monitoring systems in xEVs can lead to enhanced vehicle reliability, reduced maintenance costs, and improved overall safety, making electric vehicles more viable for widespread adoption. Moreover, this approach contributes to the reduction of downtime, maintenance costs, and safety risks in high-stakes applications like electric vehicles.