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5
Conclusions
A methodology for creating meta-models for energy consumption from surrogate data
from simulation has been demonstrated. The method was used to determine the main
effects and a simple meta-model that can be run in embedded processors in the ve-
hicle or on off-board computing platforms has been developed. The energy consump-
tion meta-model enables vehicle functions such as; distance to empty, remaining
charge needed and low-energy routing. These in turn enable vehicle features that give
the driver greater confidence in the product and therefore improves acceptance and
deployment of electric vehicles.
The traffic simulation code VISSIM and the propulsion modelling code sCVSP
were combined via a surrogate meta-model to provide energy consumption data for
developing a comprehensive energy consumption meta-model.
By developing different scenarios it was possible to determine main effects such as
road gradient and vehicle speed. It is widely believed that the driver has a large im-
pact on energy consumption. From our results we see this is likely mostly a factor of
the driver's desired speed, how it is moderated by traffic. Probably route choice is a
significant factor that would differentiate drivers.
Road gradient is another important factor, but is mostly a factor of the potential
energy build-up or loss going up or down a hill. BEV are different from hybrid elec-
tric and conventional vehicles in that much of the energy lost going uphill is regained
coming down. Unlike hybrid vehicles, the BEV battery is large enough to recover this
energy on many hills.
A third main effect is accessory loads. These come from many sources in a BEV,
but the largest factor is generally warming or cooling the vehicle. This can easily
account for 50% of the energy consumed by the vehicle and is the only factor that
favours faster travel time.
Each of these three areas; vehicle speed, road gradient and climate control require
further study. Vehicle speed on an un-crowded road is largely a factor of how fast the
driver wishes to drive. This may vary depending on the speed limit and on safety
considerations. On a crowded road vehicle speed also can be determined by how ef-
fective a driver wishing to travel quickly is at maintaining this desired speed while
interacting with slower moving vehicles. The biggest factor in velocity drag is aero-
dynamic drag which is assumed in our models to increase with the square of the ve-
locity. However, the situation on the public roads is quite a bit more complicated
where there is wind, ground turbulence, air density, disturbance by nearby traffic and
other factors to consider.
Road gradient is expected to always be a major contributor to energy consumption,
but other road factors that are not included in our model are soft road surfaces, partial-
ly inflated tires and many other factors. For the energy consumption meta-model to be
accurate it is necessary to break a road into segments that either rise or fall. If a seg-
ment goes up then down a hill, the energy consumption will not be accurate. How real
road surfaces will be broken into segments that provide good results must be consi-
dered.
Finally, the predicting of accessory loads is quite complex and is the topic of another
paper. Loss by the climate control system is controlled by several factors. External fac-
tors include ambient temperature, humidity and sun load. The vehicle configuration is a
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