Abstract:

In order to solve the problem of comfort and economy of hybrid electric vehicle (HEV) queues passing through continuous traffic signal intersections in a smart grid environment, a hierarchical control method of ecological driving and energy management based on networked HEV queue was proposed. The upper-level controller developed a target speed planning model for intersections with continuous traffic lights. Based on the defined target speed range, longitudinal constraint limits were established, and an objective function encompassing safety, comfort, following behavior, economy, and passability was formulated. The multi-objective function was solved using a model predictive control (MPC) algorithm to determine the optimal vehicle speed. Meanwhile, the lower controller adopted deep reinforcement learning (DQN) algorithm to optimize the energy management of the HEV, and took the optimal speed solved by the upper layer as the input of the lower layer to obtain the optimal output of the engine motor. The results show that the proposed control strategy can ensure the driving safety of the car queue, and the average fuel consumption of the eco-driving car queue is reduced by 8.51% compared with that of the ordinary queue, improving the ride comfort and fuel economy while avoiding the parking wait.

Key words: hybrid electric vehicle (HEV), vehicle queue, eco-driving, traffic signal, model predictive control, deep reinforcement learning (DQN)