电动汽车横摆力矩拉盖尔函数模型预测控制

针对后轮轮毂电机驱动电动汽车横向稳定性的控制问题,建立了分层控制结构。上层控制器基于拉盖尔函数模型预测控制理论,跟踪理想横摆角速度,同时考虑附加横摆力矩约束,得出附加横摆力矩需求;下层控制器以后轮轮胎利用率为目标函数,考虑轮胎附着约束和执行器约束,通过求解二次规划问题将附加横摆力矩分配到两个后轮。选取前轮转角阶跃输入和双移线2种工况,利用Carsim与Matlab/Simulink进行了联合仿真,仿真结果表明:在高附着路面条件下,所设计的控制方法减小了汽车瞬态响应的反应时间和超调量,提升了汽车瞬态响应的品质;在低附着路面条件下,所设计的控制方法使汽车能跟踪理想横摆角速度,避免汽车转向失稳。

Yaw moment control of electric vehicles based on model predictive theory using Laguerre functions

To improve the lateral stability of electric vehicles driven by rear wheel hub motor, a hierarchically coordinated vehicle dynamics control approach is presented. The high-level controller is based on the theory of model predictive control using Laguerre functions and it is designed to determine generalized moment by tracking the desired yaw angular velocity which is limited by the constraint of additional yaw moment. The low-level controller takes the rear wheels'' work-load rate as the objective function and distributes the additioral yaw moment to the two rear wheels by solving quadratic programming. And the constraint problems caused by the road adhesion and actuators are considered in the quadratic optimization. The control method is verified by Carsim and Matlab/Simulink under the conditions of a single step steering input and double-lane change, respectively. Simulation results show that under the condition of high adhesion road, the control method can reduce the reaction time and overshoot, and improve the quality of the transient response of vehicles; under the condition of low adhesion road, it makes cars track the desired yaw angular velocity and avoid cornering instability.