AI-Powered Fault Prediction and Optimization in New Energy Vehicles (NEVs) for the US Market

Abstract

The automotive industry in the USA is going through a significant transformation as global efforts to mitigate climate change and diminish greenhouse gas emissions intensify. Focal to this Paradigm shift is the advancement of New Energy Vehicles (NEVs), which comprise electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and hydrogen fuel cell vehicles (FCEVs). This research project aimed to examine the deployment of AI in forecasting and optimizing fault management in NEVs. This study intended to leverage machine learning algorithms with data analytics to provide high reliability and operational efficiency within the US automotive industry with NEVs. The dataset for the present study was accessed from accredited automotive manufacturing companies. The dataset was designed to predict the faults and optimize maintenance at NEVs. It covered simulated real vehicle data, such as sensor readings, environmental factors, driving patterns, and maintenance logs needed to understand performance, diagnose faults, and optimize a vehicle's maintenance schedule. Different algorithms were selected, such as Random Forest Classifier, Gradient Boosting Classifier, and Logistic Regression with other advantages, depending on the dataset's characteristics and the problem's complexity. Performance evaluation of the model was done with several metrics, most notably precision, recall, and F1-score. The results demonstrated that the Random Forest model attained the highest accuracy, followed closely by Gradient Boosting. AI-driven fault prediction models brought into play would greatly raise the level of impact that can be caused to the automotive industry in the US concerning the enhancement of NEV reliability and efficiency. Interpretation of the model's predictions is important in fault management strategies because it converts raw predictive outputs to actionable insights.