Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management for Plug-in Hybrid Electric Truck Platoons

To optimize fuel economy for platooning plug-in hybrid electric trucks, this paper proposes a co-optimization framework that integrates cooperative adaptive cruise control and energy management to enhance driving safety and fuel efficiency in complex traffic environments. The control strategy is divided into two layers: in the upper layer, a cooperative adaptive cruise control model based on distributed model predictive control (DMPC) is used to achieve stable platoon following and vehicle spacing, thus improving the overall platoon efficiency. In the lower layer, a distributed soft actor-critic (DSAC) algorithm is used for the fine-grained power distribution of plug-in hybrid electric trucks, enabling efficient energy utilization. The results demonstrate that this strategy significantly enhances the fuel economy and vehicle-following performance of plug-in hybrid truck platoons. Compared with the classical deep deterministic policy gradient (DDPG) algorithm, the energy management strategy based on the distributed soft actor-critic offers higher computational efficiency.