Learning-based hierarchical cooperative eco-driving with traffic flow prediction for hybrid electric vehicles

The integration of autonomous driving and hybrid electric vehicle technologies presents a promising solution for achieving environmental sustainability. This paper introduces an innovative energy-efficient driving strategy for hybrid electric vehicles that incorporates real-time traffic flow prediction. The study delves into the impact of both lateral and longitudinal vehicle maneuvers on energy consumption within dynamic traffic environments, offering novel insights into optimizing energy utilization. Firstly, a multi-lane traffic flow state rolling predictor is constructed based on the Hankel dynamic mode decomposition algorithm. Subsequently, a vehicle longitudinal and lateral coordinated control strategy is established by integrating the prioritized experience replay double deep Q-network algorithm. Finally, a novel energy management strategy is proposed that leverages Simulink dynamic model and the deep deterministic policy gradient algorithm to address the vehicle dynamic decision-making planning results. Within a hierarchical cooperative optimization framework, this research comprehensively considers safety, comfort, traffic efficiency, and fuel economy. By introducing a novel hierarchical collaborative ecological driving framework, we have achieved a substantial improvement in environmental sustainability, with traffic efficiency increasing by 10.27%-14.41% and fuel economy rising by 9.44%-10.47%. Hardware-in-the-loop validation has confirmed the proposed approach's real-time capabilities and promising practical applications.