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针对四驱混合动力汽车,为改善其电子稳定性系统(electronic stability program,ESP)车速估计的精度和鲁棒性,考虑后轮毂电机转矩精确可测可控,以及前轮驱动转矩可推算的特点,结合ESP系统既有的传感器输出信号,提出了分布式自适应无迹卡尔曼车速估计算法.首先,建立了四驱混合动力汽车动力系统及动力学模型,其包括驱动系统模型、7自由度车辆动力学模型和Burckhardt轮胎模型.其次,考虑模型的时变及强非线性特性,采用无迹卡尔曼滤波(unscented Kalman filter,UKF)算法设计了主/子滤波器.一方面通过对量测噪声的自适应,改善了UKF算法量对量测信号干扰的鲁棒性;另一方面将主/子滤波器结果进行融合,并用融合后结果重置了各滤波器,提高了车速估计精度.最后,搭建了四驱混合动力汽车Carsim-Simulink联合离线仿真平台和硬件在环仿真试验平台,分别开展了双纽线、低速双移线两种驱动工况的离线仿真和硬件在环仿真试验.试验结果表明,所提出的分布式自适应无迹卡尔曼车速估计算法不仅估计精度较高,而且也有着较强的自适应性和鲁棒性能.
In order to improve the accuracy and robustness of the vehicle speed estimation of the four-wheel drive hybrid electric vehicle, the motor torque of the rear hub is accurately measurable and controllable, and the torque of the front wheel drive can be estimated , A distributed adaptive adaptive unscented Kalman speed estimation algorithm is proposed based on the existing sensor output signals of the ESP system.Firstly, a dynamic system and a dynamic model of a four-wheel drive hybrid electric vehicle are established, including the drive system model, 7 Degree of freedom vehicle dynamics model and Burckhardt tire model.Secondly, the unscented Kalman filter (UKF) algorithm is used to design the main / subfilter considering the time-varying and strongly nonlinear characteristics of the model.On the one hand, The adaptive noise of the measured noise improves the robustness of the UKF algorithm to the interference of the measured signal. On the other hand, the main / sub-filter results are fused and the filters are reset with the result of the fusion to improve the speed estimation Precision.Finally, a four-drive hybrid vehicle Carsim-Simulink joint offline simulation platform and a hardware-in-the-loop simulation test platform were set up, Simulation of off-line driving conditions and hardware-in-the-loop simulation are carried out.The experimental results show that the proposed distributed adaptive unscented Kalman speed estimation algorithm not only has higher estimation accuracy but also has stronger self-adaptability and robustness performance.