Abstract: The control objectives for yaw stability in current distributed drive electric vehicles were relatively simplistic. To address this issue， a multi-objective torque coordination control strategy was proposed based on nonlinear MPC. The upper-level controller was a nonlinear MPC speed and yaw moment controller based on a 7-degree-of-freedom vehicle model. And the lower-level controller focused on both of achieving stability and economic efficiency through multi-objective torque coordination. The road adhesion utilization and hub motor power loss were considered in the objective function to achieve dynamic and coordinated distribution of torques. Finally， using the CarSim/Simulink joint simulation platform， the proposed method was compared with traditional control methods under the double lane change conditions and driving cycle conditions for low adhesion coefficient. The simulation results indicate that the proposed control strategy enables better tracking of the vehicles sideslip angle and yaw rate to their ideal values， thereby enhancing vehicle stability compared to that of conventional methods. Additionally，  motor energy loss is reduced and vehicle economic efficiency is improved. 

Key words: distributed drive electric vehicle, yaw stability, torque coordination, economic efficiency, model predictive control（MPC）

摘要： 针对目前分布式驱动电动汽车存在横摆稳定性控制目标单一的问题，提出了一种基于非线性模型预测控制（MPC）的多目标扭矩协调控制策略。上层控制器为基于七自由度车辆模型的非线性MPC速度及横摆力矩控制器，下层控制器为兼顾稳定性和经济性的多目标扭矩协调控制器，在目标函数中考虑了路面附着利用率和轮毂电机功率损耗，可实现扭矩动态协调分配。最后，选用低附着系数的双移线工况和驾驶循环工况，在CarSim/Simulink联合仿真平台上将所提方法与传统方法相比较。仿真结果表明：所提出的控制策略能够使车辆质心侧偏角、横摆角速度较好地追踪理想值，车辆稳定性较传统方法得到了提高，同时能够降低电机能量损耗，改善车辆的经济性。

关键词: 分布式驱动电动汽车, 横摆稳定性, 扭矩协调, 经济性, 模型预测控制（MPC）