Multifaceted multi-criteria decision making framework to prioritise the electric two-wheelers based on standard and regional driving cycles

Abstract

The primary objective of the carried-out research work is to utilise the multi-criteria decision-making (MCDM) techniques to rank the top-selling electric two-wheeler (E2W) models based on the different performance features stated by original equipment manufacturers and estimated through standard and regional driving cycle (DC) analysis. First and foremost, the vehicle structural, powertrain, performance features, and cost component-related data of eight top-selling E2W models (M1–M8) are gathered from the official websites of OEMs and local dealers. Then, performance features like energy consumption per km (EC/km), driving range (DR), well-to-wheel (WTW) CO2 emission, total cost of ownership (TCO), and total cost of ownership per km (TCO/km) are estimated under standard and regional DCs through the developed energy consumption and TCO models. Further, the ten different MCDM tools are utilised for prioritising the E2W models under each DC with the attributes’ weights estimated from the entropy methods. To enhance the trustworthiness of ranking results, sensitivity analysis is done using six distinct combinations of attribute weights. Moreover, the M7 E2W is identified as the best E2W by utilising Borda Count to integrate all the ranking results. Overall, this study provides an in-depth understanding for users about how to select E2W with minimal effort.