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Most navigation systems use data from satellites to provide drivers with theshortest-distance, shortest-time or highway-preferred paths. However, when therouting decisions are made for advanced vehicles, there are other factorsaffecting cost, such as vehicle powertrain type, battery state of charge (SOC)and the change of component efficiencies under traffic conditions, which arenot considered by traditional routing systems. The impact of the trade-offbetween distance and traffic on the cost of the trip might change with the typeof vehicle technology and component dynamics. As a result, the least-cost pathsmight be different from the shortest-distance or shortest-time paths. In thiswork, a novel routing strategy has been proposed where the decision-makingprocess benefits from the aforementioned information to result in a least-costpath for drivers. We integrate vehicle powertrain dynamics into routeoptimization and call this strategy as Vehicle Powertrain Connected RouteOptimization (VPCRO). We find that the optimal paths might change significantlyfor all types of vehicle powertrains when VPCRO is used instead ofshortest-distance strategy. About 81% and 58% of trips were replaced bydifferent optimal paths with VPCRO when the vehicle type was ConventionalVehicle (CV) and Electrified Vehicle (EV), respectively. Changed routes hadreduced travel costs on an average of 15% up to a maximum of 60% for CVs and onan average of 6% up to a maximum of 30% for EVs. Moreover, it was observed that3% and 10% of trips had different optimal paths for a plug-in hybrid electricvehicle, when initial battery SOC changed from 90% to 60% and 40%,respectively. Paper shows that using sensory information from vehiclepowertrain for route optimization plays an important role to minimize travelcosts.

Vehicle Powertrain Connected Route Optimization for Conventional, Hybrid and Plug-in Electric Vehicles