Comparing the Economic and Environmental Compatibility of Battery Electric and Conventional Vehicles in India

Abstract

Conventional vehicle fuel resources are encompassed with the jeopardy of being scarce; eventually exacerbating fuel prices. This high fuel prices further aggregate to elevate the total ownership cost and have led to an epiphany of national energy security. Further, the emission from conventional fuel combustion urges a need to cogitate about the already saddled environmental concerns. Alternatively, electric vehicles are looked upon as a potential option to conventional vehicles due to no tail-pipe emissions and low operating costs. However, if a complete life cycle is considered, an intuitive assumption that electric vehicles have no emissions and costs less can be a deception. Hence, the feasibility of electric vehicles as an option for conventional vehicles needs to be contemplated in economic and environmental aspects. This article presents the comparison between battery electric vehicles and conventional vehicles by performing a life cycle analysis (economic and environmental) in the Indian context. A Total Cost of Ownership (TCO) model is developed for financial analysis to depict the compatibility status of battery electric vehicles. The environmental analysis is conducted by using OpenLCA software based on ReCePi 2016 method for all the impact categories at mid-point as well as end-point levels. The results reckon electric vehicles are costlier than conventional vehicles with current statistics and policies in India. However, by implementing certain optimizing parameters in sensitivity analysis, electric vehicles are found to have cost parity and even become more economical than conventional vehicles in some cases. The outcomes from environmental analysis unveil that the GHG emissions from battery electric vehicles are less than that from conventional vehicles. However, out of the 18 impact categories considered, battery electric vehicles have less impact in 10 categories and even have less impact score at the end-point level.