4WD车辆路面附着系数自适应滤波估计方法

4WD Vehicle Road Adhesion Coefficient Estimation Method Based on Adaptive Filter

分布式驱动车辆路面附着系数是开发先进底盘控制策略的关键,而轮胎参数对路面附着系数估计具有较大影响。因此,提出了考虑轮胎参数的4WD车辆路面附着系数自适应滤波估计方法,提高了附着系数估计的准确性。在建立HSRI轮胎模型和3DOF车辆模型的基础上,基于扩展卡尔曼滤波理论开发了4WD车辆路面附着系数估计模型;为提高算法估计的精度,以轮胎半径变化量为输入设计了EKF参数自适应模糊调节器,通过对EKF量测噪声实时调整的方式实现了路面附着系数估计。开展实验验证了算法的可靠性,并在双移线和对接路面工况进行了仿真分析。结果表明,相比于EKF算法,该算法在双移线低附着路面上精度提高了17.2%,在对接路面工况下精度整体提高了约35.7%。研究结果为分布式驱动车辆的精确力矩分配提供了理论基础。

The key of developing advanced chassis control strategy was road adhesion coefficient of distributed drive vehicle.However,tire parameters exercised great influence on the estimation of road adhesion coefficient.Therefore,in order to improve the accuracy of adhesion coefficient estimation,the method of 4WD vehicle road adhesion coefficient estimation based on self-adaptive filter considering tire parameters was proposed.Based on HSRI tire model and 3DOF vehicle model,the estimation model of 4WD vehicle road adhesion coefficient was developed based on extend kalman filter theory.Aiming at the improvement of the accuracy of algorithm estimation,an self-adaptive fuzzy regulator with EKF parameters was designed and the changes of tire radius was as the input of the regulator.Self-adaptive tuning of estimation of road adhesion coefficient was achieved by real-time adjusting measurement noise.Experiments were carried out to verify the reliability of the algorithm and simulation analysis was used under double lane low road and joint road.The results showed that compared with EKF algorithm,the accuracy of the selfadaptive algorithm estimation was improved by 17.2%under double lane low adhesion road and round 35.7%under joint road respectively.The simulation results provided a theoretical basis for the accurate torque distribution of distributed drive vehicle.