智能分布式驱动汽车路径跟踪/制动能量回收协同控制策略研究

Investigation of Cooperative Control Strategy for Path Tracking and Braking Energy Recovery for Intelligent Distributed-drive Vehicles

为了解决智能分布式驱动汽车路径跟踪与制动能量回收系统间的协同控制难题,充分考虑分布式驱动汽车四轮扭矩独立可控在智能驾驶系统中的优势,设计适应不同路面附着条件的智能分布式驱动汽车转向、制动分层协同控制策略。上层控制器依据不同的路面类型设计差异化的多目标代价函数,以综合优化各工况下的控制目标。高附路面下,制定满足最大能量回收值的全局参考车速,在线优化路径跟踪指令,实现最优能量回收的同时减小系统运算负荷;低附路面下,优先考虑车辆的路径跟踪性能和行驶稳定性,在多目标代价函数中取消对全局参考车速的跟随要求,增设终端速度约束与能量回收项性能指标并减小能量回收项性能指标的权重系数。上层控制器基于模型预测控制方法对多目标代价函数进行滚动优化与预测求解,得到期望的前轮转角及4个车轮的总制动扭矩需求。下层控制器根据制动扭矩需求对四轮的液压制动扭矩和电机制动扭矩进行分配,最终完成整个复合制动过程。基于MATLAB/Simulink和CarSim软件,搭建控制器在环仿真平台,并在高附和低附路面条件下对所提出的策略进行试验验证。研究结果表明:高附路面下,所提出的控制策略在准确跟踪期望路径的同时相较固定比例制动力分配方法可提升2.7%的能量回收值并减少约0.02 s的单次计算时间;低附路面下,与使用高附控制策略相比,能够保证车辆的路径跟踪准确性与行驶稳定性,同时可提升7.8%的能量回收值;控制器在环试验结果证明了该协同控制策略对车辆性能提升的有效性。

This study is concerned with cooperative control between the path tracking and the braking energy recovery system of intelligent distributed-drive vehicles. Specifically, the advantages of the independent controllable four-wheel torque of a distributed-drive vehicle were considered in an intelligent driving system, and a layered cooperative control strategy was developed for the steering and braking system of intelligent distributed-drive vehicles to adapt to different road adhesion conditions. In the upper-layer controller, differential multi-objective cost functions were used according to road type to comprehensively optimize the control objectives under various driving conditions. The reference speed maximizing energy recovery on high-adhesion roads was determined, and path tracking was optimized online to achieve better energy recovery and reduce computing load. On low-adhesion roads, path tracking performance and driving stability were emphasized, and the speed tracking requirement was replaced by a terminal speed constraint and an energy-recovery performance indicator. Furthermore, the weight coefficient of the energy-recovery performance indicator in the cost function was reduced. The upper-layer controller performed predictive solution and rolling optimization based on the model predictive control method, and obtained the desired front-wheel steering angle and the total braking torque requirement. The lower-layer controller distributed the hydraulic braking torque and motor braking torque according to the total braking torque requirement, and completed the entire composite braking process. A controller-in-the-loop simulation platform was constructed based on MATLAB/Simulink and CarSim, and the proposed strategy was tested and verified on high-and low-adhesion roads. The results on high-adhesion roads indicate that the proposed control strategy can accurately track the desired path and increase the energy recovery value by 2.7% compared with the fixed proportional braking force distribution method, and reduce the single calculation time by approximately 0.02 s;On low-adhesion roads, the strategy can ensure the path-tracking accuracy and driving stability compared with the control strategy on high-adhesion roads. Furthermore, it can increase the energy recovery value by approximately 7.8%. The results of the controller-in-the-loop test demonstrate the effectiveness of the cooperative control strategy in improving vehicle performance.