The need to reduce pollution, relatively low cost of ownership, and the recent news about exhaust fumes emissions tampering have the potential to increase electric car sales. However, electric cars also need a network of fast charging stations to allow a quickly recharge when driving relatively long routes. This could be achieved by locating charging station for instance in motorway service areas. Additional issues include finding an ideal source for the needed recharge energy. The aim of this study is to develop a predictive study in order to enable the widespread diffusion of electric mobility and to optimize the deployment of the charging infrastructure necessary for its sustainability. In this paper, a preliminary model toward such goal will be proposed, including a discrete event simulator that could be used both to identify critical variables parameters and terms of the model, as well as to calibrate the parameter values from measured data.

A predictive model to support the widespread diffusion of electric mobility

Longo, Michela;Maffezzoni, Paolo;Zaninelli, Dario;Daniel, Luca
2017-01-01

Abstract

The need to reduce pollution, relatively low cost of ownership, and the recent news about exhaust fumes emissions tampering have the potential to increase electric car sales. However, electric cars also need a network of fast charging stations to allow a quickly recharge when driving relatively long routes. This could be achieved by locating charging station for instance in motorway service areas. Additional issues include finding an ideal source for the needed recharge energy. The aim of this study is to develop a predictive study in order to enable the widespread diffusion of electric mobility and to optimize the deployment of the charging infrastructure necessary for its sustainability. In this paper, a preliminary model toward such goal will be proposed, including a discrete event simulator that could be used both to identify critical variables parameters and terms of the model, as well as to calibrate the parameter values from measured data.
2017
5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings
9781509064847
agent based model; charging station; electric vehicle; event driven; nodal analysis; planning; simulation; Modeling and Simulation; Transportation; Computer Networks and Communications; Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1071499
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