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

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 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 leastcost 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)”. In order to show the cost benefits of VPCRO, the Dijkstra’s algorithm was employed to solve the leastcost problem on a traffic network model based on Shanghai, China with an average traffic information collected from Google Maps. The performance of the proposed routing strategy was compared to the shortest-distance and shortest time routing strategies. It was found that the optimal paths might change significantly for all types of vehicle powertrains. About 81% and 58% of trips were replaced by different optimal paths with the proposed VPCRO strategy 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.