考虑不同路面附着情况的整车质量估计方法研究

Study on Vehicle Mass Estimation Method Considering Different Path Adhesions

利用带有遗忘因子的递推最小二乘估计,实现了一种整车质量估计方法,该方法考虑了不同路面附着情况对整车质量估计过程的影响,通过引入路面特征系数,实现了算法对不同路面附着情况的适应。基于Matlab/Simulink编写辨识算法并在CarMaker中完成仿真测试,测试结果表明所实现的辨识方法对整车质量估计的精度可控制在10%之内。此外,由于递推最小二乘估计方法和卡尔曼滤波方法之间的内在联系,通过推导表明了递推最小二乘估计方法是状态转移矩阵为单位阵的卡尔曼滤波方法的特殊形式,为2种估计方法的工程应用形式及调试过程提供了更全面的信息。

Using recursive least squares estimation with forgetting factor,a vehicle mass estimation method was realized.The impact of different road attachment conditions on the vehicle mass estimation process was considered and a path characteristic parameter was proposed.An estimation algorithm was written based on Simulink and completed in CarMaker Simulation test.The test results show that the accuracy of the realized identification method on the overall vehicle mass estimation can be controlled within 10%.In addition,due to the inherent relationship between the recursive least squares estimation method and the Kalman filtering method,it is deduced that the recursive least squares estimation method is a special form of the Kalman filtering method with the state transition matrix as the unit matrix.This provides more comprehensive information for the engineering application form and debugging process of the two estimation methods.