Reliability Evaluation of Autonomous Electric Vehicles Using Fault Tree Method

This paper proposes an advanced method using fault tree analysis (FTA) to evaluate the reliability of autonomous electric vehicles (AEVs). As the utilization of AEVs for transportation continues to rise, there has been a notable surge in the scrutiny of their reliability. The reliability objective in this paper is to maintain the safe operation of AEVs by analyzing the effects of essential subsystems such as the steering system, automation system, and powertrain system on the AEVs. Reliability is evaluated using a level 3 driving conditional automation vehicle. Relyence software and Monte Carlo simulation are used to develop the fault tree (FT) of the proposed AEV to find its reliability indices. Manufacturers and reliability engineers can foresee the overall system's reliability and make necessary adjustments to improve system design by identifying the possible sources of failure in these systems that could cause the reliability objective to fail. Using the criticality importance (CRIT), risk achievement worth (RAW), risk reduction worth (RRW), and time interval method, the importance of AEV components in influencing the system reliability is quantified in this study. Based on these results, the reliability can be improved in the design stage by finding vulnerabilities in system components. The proposed method is applied to AEV level 3 to show its efficacy.