Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety

Abstract

This paper presents a novel torque vectoring control (TVC) method for four in-wheel-motor independent-drive electric vehicles that considers both energy-saving and safety performance using deep reinforcement learning (RL). Firstly, the tire model is identified using the Fibonacci tree optimization algorithm, and a hierarchical torque vectoring control scheme is designed based on a nonlinear seven-degree-of-freedom vehicle model. This control structure comprises an active safety control layer and a torque allocation layer based on RL. The active safety control layer provides a torque reference for the torque allocation layer to allocate torque while considering both energy-saving and safety performance. Specifically, a new heuristic random ensembled double Q-learning RL algorithm is proposed to calculate the optimal torque allocation for all driving conditions. Finally, numerical experiments are conducted under different driving conditions to validate the effectiveness of the proposed TVC method. Through comparative studies, we emphasize that the novel TVC method outperforms many existing related control results in improving vehicle safety and energy savings, as well as reducing driver workload.