Research on Deep Reinforcement Learning-based Intelligent Car-following Control and Energy Management Strategy for Hybrid Electric Vehicles

Abstract

The aims is to study the control technology of intelligent hybrid electric vehicles(HEVs) and deep reinforcement learning (DRL) algorithms. Firstly, under the car-following model of two HEVs, a deep q-network(DQN)-based energy management strategy (EMS) for the leading car is proposed, which realizes the multi-objective collaborative control of the engine and the continuous variable transmission(CVT) by DRL. Secondly, a hierarchical control model based on DRL is established for the following car, which realizes the upper-level car-following control and lower-level energy management facing to an intelligent HEV. Finally, a simulation verifies the effectiveness of the hierarchical control model. The results show that the DRL-based car-following control strategy has ideal tracking performance. Meanwhile, the DRL-based EMS achieves good fuel economy in both the leading car and the following car. Moreover, the average time of outputting each set of actions is 1.66ms for the DRL-based EMS, which ensuring the potential for real-time applications.