PREDICTIVE ANALYSIS OF FAULTS IN ELECTRIC VEHICLES

Abstract

Electric vehicle (EV) development has produced a number of advantages in terms of sustainability and energy efficiency. The dependability and safety of EV systems must still be ensured, though. In-depth study of predictive modeling methods for EV fault finding is provided in this research. The work focuses on the creation and use of defect prediction models utilizing real-time data gathered from EV systems, building on the foundations of predictive analysis and machine learning. The approach for data collecting, preprocessing, feature extraction, and model training is described in detail, with an emphasis on the necessity of reliable and representative data for successful fault diagnosis. Additionally, it analyzes the performance criteria employed for assessing the precision and dependability of these models and illustrates the difficulties in incorporating predictive analytic models into EV designs. The research results illustrate how predictive analysis may be successfully applied to problem detection and diagnosis, highlighting its potential to improve the safety and effectiveness of EV systems. In order to achieve more robust and accurate defect prediction in EVs, the article finishes with insights into future research areas, highlighting the need for more breakthroughs in data gathering techniques, model optimization, and real-time monitoring systems. KEYWORDS—Electric vehicles; predictive analysis; fault detection; machine learning; data collection; data preprocessing; feature extraction; model training; performance metrics; real-time monitoring;