Electrification of automobile sector in India

Abstract

The automobile and transportation sector has over the last decade come into intense focus due to its strong association with issues like global warming, environmental pollution and volatility in fossil fuel prices. Various approaches have been considered, eg: fuel cells, biofuels, efficient ICEs. While these solutions have been adopted to some extent in some regions, they fail to resolve many of the important issues or bring along their own problems. However, there is one promising approach which comes close to a cure-all and has generated significant interest, that of Electrification of vehicles. Electric vehicles have zero emissions on the road, and are more efficient than conventional vehicles. However, a critical factor is required to ensure their mainstream adoption, affordable and high capacity batteries. Current battery technology is still nascent, and the batteries developed are extremely expensive as a proportion of the energy they can carry. Moreover they take a long time to recharge, further obstructing their adoption. To accelerate technological development and cost reduction, volume production and considerable R&D resources are required. However, given the positive externalities and the steep learning curve of batteries, the government can provide subsidies and assist in market development. Moreover, if the positive externalities are monetized, the burden on the government can be reduced. We constructed a Stella model to explore the relationship between population of a region, electric bus adoption and pollution levels. Public buses were selected for analysis since a high proportion of MMR’s population travels using them, and the buses on average travel long distances every day. Hence introduction of electric buses is expected to be high impact. We based our model in the Mumbai Metropolitan region (MMR) which consists of the main city of Mumbai, suburbs and exurbs. We then calibrated/validated our model according to past data and evaluated different scenarios for the future.