Car-Following Safe Headway Strategy with Battery-Health Conscious: A Reinforcement Learning Approach

摘要

This paper proposes an optimal car-following strategy for pure electric vehicles (EVs) with the aim of keeping an expected headway of the leader and reducing vehicle battery loss. In particular, a car-following system model is established. The primary task of the automatic vehicle is to follow the trajectory of the preceding car and maintain an expected headway. Then, the paper analyzes the powertrain of the electric vehicle. The loss of battery life over a period of time is proportional to the acceleration, so it takes the battery life into consideration. The Q-learning algorithm is conducted for the optimal car-following strategy using system data instead of system dynamics information. It utilizes reward function and greedy strategy to select actions to train the following vehicle to achieve car-following safety. When there is no collision in these two cars, acceleration is considered into reward function to reduce battery loss. Finally, it is verified by simulation that the proposed car-following strategy can keep good tracking, maintain the expected headway from the preceding vehicle, and reduce battery loss.