Abstract: With the advantage of huge information source for distributed drive electric vehicle, a tire lateral force observer is set up based on the Extended Kalman Filter algorithm（EKF）. According to the linear modified 2DOF vehicle model, the target of control system is established. The sliding mode controller of direct yaw moment is designed based on the estimation of tire lateral force. Considering both tire load rate and driving motor efficiency, the comprehensive driving force optimization distribution achieves the stability and energy efficiency coupling control of vehicle. The result of the Carsim-Matlab/Simulink simulation indicates that the entire system achieves an accurate estimation of tire lateral force and improves the accuracy of the target direct yaw moment calculation. The comprehensive driving force optimization distribution increases not only the vehicle-road adhesion but also the comprehensive efficiency of driving motor. The energy efficiency of the vehicle has been improved further.

Key words: extended Kalman filter, direct yaw moment, sliding mode control, driving forces comprehensive optimization distribution

摘要： 充分利用分布式驱动汽车信息源多的特点，根据扩展卡尔曼滤波算法（EKF）建立观测器对车轮侧向力进行在线估计。通过改进的车辆线性二自由度模型制定系统控制目标，依据车轮侧向力观测值设计了基于滑模变结构控制的直接横摆力矩控制器。全轮驱动力综合优化分配策略同时考虑了轮胎负荷率与驱动电机效率，完成了对车轮稳定性与能量效率的耦合控制。通过Carsim-Matlab/Simulink的仿真表明，整个系统实现了对车轮侧向力的准确估计，提高了目标直接横摆力矩计算的准确性。驱动力综合优化分配在提高车辆路面附着余量的同时也提高了各驱动电机的综合效率，进一步提高了车辆的能量利用效率。

关键词: 扩展卡尔曼滤波, 直接横摆力矩, 滑模控制, 驱动力综合优化分配