Driving Risk Assessment of Shared Electric Vehicles Based on CODAS Method

Abstract:

Based on the fuzzy information and multi-attribute decision making， a failure mode and effect analysis （FMEA） method for shared electric vehicles（SEVs） was proposed， including determining experts candidate and their authority weights through mutual evaluation， constructing risk assessment criteria through qualitative analysis， testing expert opinions using consensus theory， objectively weighting risk factors using close degree optimization method， and ranking the risks of failure modes by introducing the combinative distance-based assessment （CODAS） method. The results show that high-risk failure modes are mainly distributed in power system and driving system. The robustness of the results and the validity of the method were verified by sensitivity analysis and comparative analysis. Finally， feasible safety management measures were proposed.