Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving

Abstract

For human–machine collaborative driving conditions, a hierarchical chassis multi-objective cooperative control method is proposed in this paper. Firstly, based on the phase plane theory, vehicle dynamics analysis is carried out to complete the definition of vehicle stability region. Secondly, based on the linear time-varying (LTV) system model, a cooperative control strategy combining fuzzy control with model predictive control (MPC) is proposed in the upper layer. In this strategy, the assisted driving weight adjustment coefficient and the stability weight adjustment coefficient are obtained by fuzzy mapping combining human–machine cooperation index and the vehicle stability region, respectively, and the optimization objectives of MPC are designed based on the above coefficients. In the lower layer torque allocation strategy, the stability weight adjustment coefficient is introduced to achieve multi-objective optimization of tire load rate and energy efficiency. For energy efficiency optimization, an optimal energy efficiency point-based tracking method is proposed to avoid nonlinearity caused by the introduction of motor loss models. Simulation analysis results show that the proposed strategy can effectively alleviate human–machine conflicts and improve vehicle handing stability. It also can achieve smaller tire load rate optimization through torque allocation and can reduce energy consumption by approximately 8% compared with the inter-axle torque allocation strategy. This study helps to promote the improvement of the comprehensive performance of assisted driving vehicles in human–machine cooperation, handling stability, and energy-saving torque distribution.

Energy-Saving Impact and Optimized Control Scheme of Vertical Load on Distributed Electric Wheel Loader

“…For multi-axle distributed vehicles, stability weight adjustment coefficients were introduced in the torque distribution strategy to achieve the multi-objective optimization of the tire load rate and energy efficiency. The simulation analysis results showed that the torque distribution can be reduced by approximately 8% in energy consumption compared to the inter-axle torque allocation strategy [22]. To reduce energy consumption during the steering condition for multi-axle electric vehicles, the deep deterministic policy gradient (DDPG) algorithm was proposed, resulting in a decrease of approximately 5% in the maximum SOC degradation rate of the vehicle [23].…”