Hierarchical energy management of plug-in hybrid electric trucks based on state-of-charge optimization

To solve the contradiction between the optimality of the energy management strategy and the adaptability of the driving conditions of the hybrid electric vehicle. This paper proposes a hierarchical energy management strategy for plug-in hybrid electric trucks based on the state of charge (SOC). In the upper hierarchical layer, the deep deterministic policy gradient (DDPG) algorithm is used to generate the SOC reference value for the future driving section based on the historical driving condition information, to guide the SOC of the battery and make the SOC run in a reasonable range. Ultimately, it enables optimal battery discharge for plug-in hybrid electric trucks throughout the journey. In the lower hierarchical layer, the long short-term memory (LSTM) neural network algorithm is used to predict vehicle speed in the short term. Meanwhile, the model predictive control of the vehicle is constructed to distribute the required power of the plug-in hybrid electric truck in real-time, and the penalty factor is introduced into the objective function to accurately follow the SOC reference value. The simulation results show that the control strategy in this paper saves 16.34 % of fuel compared with the chargedepleting/charge-sustaining (CD/CS) strategy, which greatly improves fuel economy. At the same time, the simulation results of different driving conditions show that the fuel-saving rate is about 16 %, which verifies the robustness of the proposed method.