  Most navigation systems use data from satellites to provide drivers with the
shortest-distance, shortest-time or highway-preferred paths. However, when the
routing decisions are made for advanced vehicles, there are other factors
affecting cost, such as vehicle powertrain type, battery state of charge (SOC)
and the change of component efficiencies under traffic conditions, which are
not considered by traditional routing systems. The impact of the trade-off
between distance and traffic on the cost of the trip might change with the type
of vehicle technology and component dynamics. As a result, the least-cost paths
might be different from the shortest-distance or shortest-time paths. In this
work, a novel routing strategy has been proposed where the decision-making
process benefits from the aforementioned information to result in a least-cost
path for drivers. We integrate vehicle powertrain dynamics into route
optimization and call this strategy as Vehicle Powertrain Connected Route
Optimization (VPCRO). We find that the optimal paths might change significantly
for all types of vehicle powertrains when VPCRO is used instead of
shortest-distance strategy. About 81% and 58% of trips were replaced by
different optimal paths with VPCRO when the vehicle type was Conventional
Vehicle (CV) and Electrified Vehicle (EV), respectively. Changed routes had
reduced travel costs on an average of 15% up to a maximum of 60% for CVs and on
an average of 6% up to a maximum of 30% for EVs. Moreover, it was observed that
3% and 10% of trips had different optimal paths for a plug-in hybrid electric
vehicle, when initial battery SOC changed from 90% to 60% and 40%,
respectively. Paper shows that using sensory information from vehicle
powertrain for route optimization plays an important role to minimize travel
costs.

Systems and Control