Fault Diagnosis Based on Database-Driven Techniques: Application to the Electric Vehicle Case

The progress of technology within the automotive sector, particularly the advent of electric vehicles, hinges on the reliability and efficacy of safety mechanisms like the anti-lock braking system (ABS). This study introduces a data-centric approach to identify and rectify shortcomings within the ABS. Through a comparative analysis of classification algorithms, this research evaluates their proficiency in detecting faults within vehicle and wheel speed sensors. The findings underscore the superiority of decision tree (DT) algorithms in terms of accuracy and effectiveness. This investigation implies that employing such algorithms could significantly bolster vehicle safety by promptly identifying potential ABS-related issues.