Coskun, SerdarYazar, OzanZhang, FengqiLi, LinHuang, CongKarimi, Hamid Reza2025-03-172025-03-1720240967-06611873-6939https://doi.org/10.1016/j.conengprac.2024.106104https://hdl.handle.net/20.500.13099/2206Connected and autonomous vehicles have offered unprecedented opportunities to improve fuel economy and reduce emissions of hybrid electric vehicle (HEV) in vehicular platoons. In this context, a hierarchical control strategy is put forward for connected HEVs. Firstly, we consider a deep deterministic policy gradient (DDPG) algorithm to compute the optimized vehicle speed using a trained optimal policy via vehicle-to-vehicle communication in the upper level. A multi-objective reward function is introduced, integrating vehicle fuel consumption, battery state-of-the-charge, emissions, and vehicle car-following objectives. Secondly, an adaptive equivalent consumption minimization strategy is devised to implement vehicle-level torque allocation in the platoon. Two drive cycles, HWFET and human-in-the-loop simulator driving cycles are utilized for realistic testing of the considered platoon energy management. It is shown that DDPG runs the engine more efficiently than the widely-implemented Q-learning and deep Q-network, thus showing enhanced fuel savings. Further, the contribution of this paper is to speed up the higher-level vehicular control application of deep learning algorithms in the connected and automated HEV platoon energy management applications.eninfo:eu-repo/semantics/openAccessConnected and automated vehiclesDeep learningDeep reinforcement learningHybrid electric vehiclesEnergy managementArticle10.1016/j.conengprac.2024.106104153Q1WOS:0013250382000012-s2.0-85204689826Q1