Electric Vehicles (EVs) are quickly becoming a very important segment of the automotive industry. However, the so-called range anxiety, i.e., the fear that a vehicle has insufficient range to reach its destination, the experience anxiety, i.e.,the fear of the hassle of public charging and the high selling price are still major barriers to a widespread adoption of electric cars. In this paper, we use real-world data from vehicle telematics devices to quantitatively assess whether range anxiety is a justified threat. Specifically, we evaluate the vehicles electrification potential based on their real driving patterns, showing that a significant percentage of traditional Internal Combustion Engine (ICE) Vehicles could be effortlessly replaced by EVs, without any impact on the owners' driving habits and with the current public charging infrastructure, only ensuring an overnight recharging.

Mining the electrification potential of fuel-based vehicles mobility patterns: A data-based approach

Zinnari F.;Strada S.;Tanelli M.;Formentin S.;Savaia G.;Savaresi S. M.
2020-01-01

Abstract

Electric Vehicles (EVs) are quickly becoming a very important segment of the automotive industry. However, the so-called range anxiety, i.e., the fear that a vehicle has insufficient range to reach its destination, the experience anxiety, i.e.,the fear of the hassle of public charging and the high selling price are still major barriers to a widespread adoption of electric cars. In this paper, we use real-world data from vehicle telematics devices to quantitatively assess whether range anxiety is a justified threat. Specifically, we evaluate the vehicles electrification potential based on their real driving patterns, showing that a significant percentage of traditional Internal Combustion Engine (ICE) Vehicles could be effortlessly replaced by EVs, without any impact on the owners' driving habits and with the current public charging infrastructure, only ensuring an overnight recharging.
2020
Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020
978-1-7281-5871-6
Boosted Trees
Event-related Potentials
Machine-Learning
Neural Networks
time-Series Classification
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1169208
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