Performance Assessment of Battery Electric Vehicles Using the TOPSIS Method

Abstract

Worldwide interest in "hybrid and battery electric vehicles" has increased recently as a result of their ability to save on fuel, lessen reliance on foreign oil, and reduce greenhouse gas emissions. The effectiveness of the sub-systems that these vehicles are built with determines their overall success in large part. It is necessary to estimate these subsystems' parameters with great accuracy to improve their performances. "Battery electric vehicles (BEVs)", an eco-friendly type of vehicle, are crucial given that the automotive industry contributes significantly to carbon emissions. Due to the recent quick growth of the BEV market, it has grown to be a substantial challenge to evaluate BEV alternatives fully from the perspective of the consumer. By examining the fundamental characteristics of each BEV, this evaluation can be made. The use of "multiple criteria decision making (MCDM)" techniques is a useful tool for making the best BEV buying choice. Therefore, six BEVs are selected as options in this work. These vehicles are then ranked using TOPSIS based on technical specifications, such as Battery capacity, Range, Top speed, Quick charge time, Acceleration and Purchasing price. In this study TOPSIS method analyses the rank of Mercedes-Benz EQS as first, Audi e-tron GT as fourth, Porsche Taycan as fifth, Audi e-tron as third, Audi RS e-tron GT as second and Mercedes-Benz EQC as sixth. So, the result from the TOPSIS method shows that Mercedes-Benz EQS is highlighted as the best choice of the selected battery electric vehicles followed by the Audi RS e-tron GT.