  Global optimization of the energy consumption of dual power source vehicles
such as hybrid electric vehicles, plug-in hybrid electric vehicles, and plug in
fuel cell electric vehicles requires knowledge of the complete route
characteristics at the beginning of the trip. One of the main characteristics
is the vehicle speed profile across the route. The profile will translate
directly into energy requirements for a given vehicle. However, the vehicle
speed that a given driver chooses will vary from driver to driver and from time
to time, and may be slower, equal to, or faster than the average traffic flow.
If the specific driver speed profile can be predicted, the energy usage can be
optimized across the route chosen. The purpose of this paper is to research the
application of Deep Learning techniques to this problem to identify at the
beginning of a drive cycle the driver specific vehicle speed profile for an
individual driver repeated drive cycle, which can be used in an optimization
algorithm to minimize the amount of fossil fuel energy used during the trip.
