This study developed a control algorithm of torque vectoring system to improve the handling performance of a green-car and evaluate the performance of vehicle dynamics to improve the controllability and stability. Firstly, this study configured the control algorithm with the supervisory controller that decides the control mode by checking the current driving status of a vehicle, target yawrate calculator that reflects the steady-state and transient-state response characteristics according to the control mode, as well as the reference yawrate calculation, desired yaw moment calculation and transferred torque calculation. The control mode consists of the agile mode that controls the torque vectoring to improve the controllability and the safe mode that controls the torque vectoring to improve stability. Secondly, this study performed the modeling of the dual motor type torque vectoring system and an EV AWD vehicle. Lastly, this study defined the driving test scenario and evaluation method as well as the quantitative performance index for each vehicle test and established the co-simulation environment for the handling test evaluation. This study found that when a vehicle is controlled by applying the torque vectoring system, handling performance was improved according to controllability and safe mode by verifying the simulation.

这项研究开发了一种转矩矢量控制系统的控制算法，以改善绿色汽车的操纵性能，并评估车辆动力学性能以提高可控性和稳定性。首先，本研究配置了带有监督控制器的控制算法，该监督控制器通过检查车辆的当前行驶状态来决定控制模式，以及根据控制模式反映稳态和瞬态响应特性的目标横摆率计算器。作为参考横摆率计算，期望横摆力矩计算和传递扭矩计算。控制模式包括控制转矩矢量以改善可控制性的敏捷模式和控制转矩矢量以提高稳定性的安全模式。其次，这项研究对双电机型扭矩矢量系统和EV AWD车辆进行了建模。最后，本研究定义了驾驶测试的场景和评估方法，以及每种车辆测试的定量性能指标，并建立了用于处理测试评估的协同仿真环境。这项研究发现，当通过应用扭矩矢量系统控制车辆时，通过验证仿真结果，根据可控性和安全模式可提高操纵性能。