Hierarchical Coordinated Optimization and Energy Management Control for Plug-In Hybrid Electric Heavy-Duty Truck Platoon in Coal Mine Transportation System

Energy utilization and transportation scheduling efficiency are essential indicators of the coal mine transportation system. This paper aims to implement systematic control through hierarchical cooperative optimization control. A novel control framework containing system, platoon, and truck layers is proposed. First, a mixed integer non-linear programming model is established to address the coal mine platoon transportation path-planning problem. Secondly, the distributed non-linear model predictive control is used to realize the longitudinal control of platoon, and the alternating direction method of multipliers algorithm is utilized to obtain the desired speed under different unidirectional communication topologies. Then, a dynamic state of charge reference trajectory-based and adaptive equivalent factor-based deep deterministic policy gradient algorithm is proposed to distribute reasonably the power demand for plug-in hybrid electric heavy-duty truck. Finally, simulation results in real coal mine transportation scenarios show that the proposed framework can achieve fast and effective planning of transportation paths. The platoon layer meets the requirement of a secure and stable following under each unidirectional communication and improves fuel economy by 5.02%. The proposed energy management strategy at truck layer attains superior results in fuel economy improvement and overall driving cost reduction, achieving a level of global optimization akin to dynamic programming.