Electric vehicles (EVs) have advantages in the aspect of energy, environment, and vehicle motion control. However, it is still not competitive enough to conventional vehicles because of the limited driving range and the high cost of the battery. Therefore, the energy efficiency is of the most importance for the control of EVs. Existing range extension control systems on EVs mostly focus on longitudinal front and rear axle torque distribution or lower-level yaw moment allocation. It is a challenge to maintain the vehicle’s stability at the cost of the minimum energy when the vehicle is cornering, this paper proposes a phase plane-based controller for EVs, focusing on the energy-efficient upper-level yaw stability control. The phase plane-based controller is automatically adaptive to driving situations through the optimization of weights on the performance of the vehicle handling and stability. Firstly, a friction constrained desired model is presented for the model-following control. Secondly, $\beta -\dot{\beta }$ phase plane analysis is conducted based on a nonlinear vehicle model to graphically identify the vehicle lateral stability in real time. The self-stable region can be determined by the vehicle velocity, the road friction coefficient, and the wheel steering angle. Then, energy optimizing (i.e. gain scheduling of LQR controllers) rules are designed based on the vehicle lateral stability identification. Finally, the proposed phase plane-based controller is evaluated and the yaw moment costs are compared to other controllers’ in a realistic 7-DOF vehicle model. The results demonstrate that the proposed controller presents an excellent yaw stability control capability, and compared to the widely used Shino’s controller, the proposed controller reduces the energy consumption by 9.68% and 3% at the ‘light’ and ‘severe’ maneuver, respectively.

电动汽车（EV）在能源，环境和车辆运动控制方面具有优势。然而，由于有限的行驶范围和电池的高昂成本，它仍不足以与常规车辆竞争。因此，能源效率对于电动汽车的控制最为重要。电动汽车上现有的范围扩展控制系统主要集中在纵向前后轴扭矩分配或较低水平的偏航力矩分配上。在车辆转弯时，以最小的能量为代价来维持车辆的稳定性是一个挑战，本文提出了一种基于相平面的电动汽车控制器，重点是节能的高层横摆稳定性控制。通过优化对车辆操纵性能和稳定性的权重，基于相平面的控制器可自动适应驾驶情况。首先，提出了一种摩擦约束期望模型，用于模型跟踪控制。第二，$\beta --\dot{\beta }$基于非线性车辆模型进行相平面分析，以图形方式实时识别车辆横向稳定性。自稳定区域可以由车速，道路摩擦系数和车轮转向角确定。然后，基于车辆横向稳定性识别，设计能量优化（即LQR控制器的增益调度）规则。最后，在实际的7自由度车辆模型中，对提出的基于相平面的控制器进行了评估，并将偏航力矩成本与其他控制器进行了比较。结果表明，所提出的控制器具有出色的横摆稳定性控制能力，与广泛使用的Shino控制器相比，所提出的控制器在“轻”和“重”操纵下分别降低了9.68％和3％的能耗。