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Table 6: Table of vehicles.
Model Brand Mass CdA Efficiency
Twizy Renault 460 0.40 0.8
i-MiEV Mitsubishi 1110 0.75 0.8
Leaf Nissan 1474 0.57 0.8
5 CONCLUSIONS
In this work we propose a new simulation framework
in which simulation environment is much closer to the
real world conditions. Here in the simulation sce-
nario it is possible to consider the altitude and re-
lated roads’ slope coefficient as well as classic pa-
rameters in the modeling of the EVs. Moreover, it
has been proved that the altitude heavily influences
the results and in particular the Energy Consumption
model. Therefore, for considering EV simulation en-
vironment and to evaluate overall behavior it is im-
portant to include the road slope coefficient during the
EV journey. In order to achieve more detailed simu-
lation results, in this work we propose a driver clas-
sification by introducing four driver classes. In fact,
drivers may influences performances in terms of trav-
eling time, road congestion and collisions because of
their behaviors. Moreover, in this work the model was
extending to classic vehicles for better monitoring en-
ergy consumption and emissions. In this way will be
possible to extend this work to design and the develop
of Intelligent Transportation System (ITS) policies in
a further work.
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