Integrating the Root Assessment Method with Subjective Weighting Methods for Battery Electric Vehicle Selection

Abstract

The global automotive industry is actively transitioning towards the production of BEVs (Battery Electric Vehicles) to significantly reduce carbon emissions and address climate change. In the context of a world striving for sustainable development, selecting the right BEV has become a crucial decision for consumers. This study pioneers the application of the RAM (Root Assessment Method) method for BEV selection among 10 available options. Each electric vehicle is described by 11 criteria, with weights calculated using two subjective weighting methods: the ROC method and the RS (Rank Sum) method. Regardless of the weighting method employed for the criteria, the RAM method consistently identifies the same optimal BEV. Furthermore, the top-ranked electric vehicles obtained using the RAM method in conjunction with either the ROC or RS weighting methods exhibit a high degree of similarity to those determined using other ranking methods and different criteria weighting approaches.