Co-optimization of velocity planning and energy management for autonomous plug-in hybrid electric vehicles in urban driving scenarios

Co-optimization of vehicle velocity planning and powertrain control for plug-in hybrid electric vehicle (PHEV) can lead to an optimal energy saving with the help of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. In this study, a real-time hierarchical effective and efficient co-optimization control strategy is designed for automated and connected PHEV to co-optimize vehicle velocity and energy management in urban driving scenarios. In the upper layer, the external traffic disturbance and powertrain characteristics are inte-grated into velocity planning via a Gaussian process (GP) model and a desired acceleration, respectively. In the power allocation layer, a double delayed Q-learning (DDQL) algorithm is employed to instantaneously optimize the power allocation for powertrain system based on the planned velocity. The feasibility and energy-saving effect of the proposed co-optimization strategy is verified through a traffic-in-the-loop simulator under various urban driving scenarios. The simulation results demonstrate that the integration of traffic lights, powertrain characteristics and speed prediction of preceding vehicle into velocity planning of PHEV can make vehicle ve-locity smoother, so as to improve fuel economy, driving comfort and traffic efficiency. As coupled with DDQL algorithm, our proposed co-optimization strategy can reach 97.31% energy economy of typical DP-based strategy but in a real-time framework.

Automated summary

In this study, a real-time hierarchical effective and efficient co-optimization control strategy is designed for automated and connected PHEV to co-optimize vehicle velocity and energy management in urban driving scenarios. The feasibility and energy-saving effect of the proposed co-optimization strategy is verified through a traffic-in-the-loop simulator under various urban driving scenarios.