Adaptive Torque Vector Control for Distributed Drive Intelligent Electric Vehicles Using Model and Data Driven Approaches

To effectively improve the performance of chassis control of distributed drive intelligent electric vehicles (EVs) under difference road conditions, especially in combing road information and chassis control for improving road handling and ride comfort, is a challenging task for the distributed drive intelligent EVs. Simultaneously, inaccurate chassis control and uncertainty with system input, are always existing, e.g., varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis control strategy of distributed drive intelligent EVs become a hot topic in both academia and industry. To issue the above mentioned, an adaptive torque vector hierarchical controller based on road roughness and adhesion is proposed, which optimizes the comprehensive performance of vehicle chassis. First, combined with the characteristic of the unbalance dynamic force caused by the air gap between the stator and the rotor of the in-wheel motor, a nonlinear vehicle model based on motor unbalanced electromagnetic force is developed. Then, using the deep neural network, an algorithm for road roughness and adhesion recognition based on system response data is designed. Meanwhile, an adaptive torque vector controller based on road information is designed to improve the driving safety and handling stability of chassis. Finally, the proposed algorithm is validated on the full-car test rig platform, results show that the proposed algorithm can improve chassis performance by no less 10% under double lane-change test. The research achievements develop a reasonable algorithm to apply to the improving road handling and ride comfort performance for distributed drive intelligent EVs.