Fuel Economic Co-optimization of Vehicle Route and Speed for Connected Vehicles

Traditionally, vehicle route planning problem focuses on route selection based on traffic data. This paper proposes a novel extended vehicle route planning problem, called vehicle macroscopic motion planning (VMMP), to optimize vehicle route and speed simultaneously for improved fuel economy within an expected trip time. The required traffic data and vehicle dynamic parameters can be collected through vehicle connectivity which is developed rapidly in recent years. A genetic algorithm based co-optimization method is used to solve the proposed VMMP problem. A co-simulation model, combining a traffic model based on SUMO (Simulation of Urban MObility) and a Simulink powertrain model, is developed and used for validating the proposed VMMP problem. The simulation results show that the proposed VMMP method is able to significantly improve vehicle fuel economy. Comparing with the fastest route, the fuel economy using the proposed VMMP method is improved by up to 15%. Additionally, a simulation study is designed for different vehicle platforms and the results show that different vehicles could have different economic routes and speed profiles.