Electric cars, assessment of green nature vis a vis conventional fuel driven cars
Authors: Satish Vitta
Abstract: A comprehensive analysis of energy requirements and emissions associated with electric vehicles, ranging from mining and making the rare-earth magnets required in electric motor to assembling the Li-ion battery, including charging and regular running of the electric vehicles has been performed. A simple, analytical procedure is used to determine the embodied energy and emissions. The objective is to assess the potential of electric cars to reduce green house gases emission to limit global warming to < 1.5 degrees C by the Year 2050 as per IPCC recommendations and also to compare them with conventional fuel driven cars. The combined embodied energy for Nd- and Dy-metals production which are required in electric motors and battery assembly for 150 million cars, projected to be on the road in the year 2050 is ~ 1500 TWh and the CO2 emissions is found to be > 600 MT. The emissions includes carbon intensity of electrical energy required to run these electric vehicles. The projected emissions due to fossil fuels, gasoline production as well as burning it in combustion engines however is only 412 MT, far less than that due to electric vehicles. The main contributor to emissions from electric vehicles is the battery assembling process which releases ~ 379 MT of CO2-e gases. The emissions from both electric vehicles as well as combustion engine vehicles scale linearly with the number of vehicles, indicating that a breakeven is not possible with the currently available manufacturing technologies. These results clearly show that significant technological developments have to take place in electric vehicles so that they become environmentally better placed compared to combustion engine based cars.
Explore the paper tree
Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant
Look for similar papers (in beta version)
By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.