Chen Te, Chen Long, Xu Xing, Cai Yingfeng, Jiang Haobin, Sun Xiaoqiang
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2018 Apr 20;18(4):1268. doi: 10.3390/s18041268.
Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.
精确估计纵向力和侧偏角对于四轮独立驱动电动汽车的横向稳定性和路径跟踪控制至关重要。本文提出了一种通过观测器迭代和信息融合来估计四轮独立驱动电动汽车纵向力和侧偏角的有效方法。将电动驱动轮模型引入车辆建模过程并用于纵向力估计，通过模型解耦得到纵向力重构方程，联合Luenberger观测器和高阶滑模观测器进行纵向力观测器设计，并应用卡尔曼滤波器抑制噪声影响。基于估计得到的纵向力，进而提出一种基于观测器迭代和信息融合的估计策略，其中应用Luenberger观测器利用较少的传感器测量实现先验估计，扩展卡尔曼滤波器用于精度更高的后验估计，模糊权重控制器用于增强观测器系统的自适应能力。进行了仿真和实验，验证了所提估计方法的有效性。
