Hierarchical control of plug-in hybrid electric vehicle platoon with DQL optimized SOC reference trajectories incorporating traffic information prediction working condition

To address the problem of the traditional linear state-of-charge (SOC) reference trajectory cannot adapt to the driving conditions, a hierarchical control of plug-in hybrid electric vehicle (PHEV) platoon with deep Q-learning (DQL) optimized SOC reference trajectories incorporating traffic information prediction working conditions is proposed. Based on the traffic information, the upper-level controller uses a long short-term memory (LSTM) neural network to achieve global speed prediction for the lead vehicle, and a nonlinear model predictive control (NMPC) algorithm to achieve longitudinal control of the platoon and obtain the optimal demand torque for the vehicle. The lower-level uses principal component analysis (PCA) and a K-means clustering algorithm to construct four typical working conditions. The radial basis function (RBF) neural network is used to predict the vehicle speed in a short time, and then the SOC reference trajectory in the prediction time domain is generated by the coupled DQL network. Under the framework of MPC, real-time energy management of the platoon is achieved through rolling optimization using the dynamic programing (DP) algorithm. The results show that the strategy can significantly improve the fuel economy of the platoon. Compared with the time-based and distance-based SOC reference trajectories, the platoon’s fuel consumption is reduced by 7.94% and 3.72%, respectively. Compared with the SOC reference trajectory based on DP, the platoon’s fuel consumption is roughly the same. However, the strategy can not only adapt to the change of driving conditions but also be applied online.