Abstract:

In order to accurately estimate the masses of heavy trucks in the PCC processes, based on the vehicle longitudinal dynamics model, a vehicle mass estimation algorithm was proposed. The Kalman filtering algorithm was used to estimate the engine output shaft torques which were used as inputs for the vehicle mass estimation algorithm. The vehicle mass was estimated based on the RLS method. C code was generated for the quality estimation control model built by MATLAB/Simulink and embedded in the development boards. And the proposed vehicle mass estimation algorithm was carried out under no-load, one-third load and full load road tests. The testing results show that the maximum errors of the proposed algorithm are 8.87% under full load, 7.43% under one-third load and 4.40% under the no-load, which may meet the mass estimation error requirement within 10% of the vehicle PCC processes and plays an important role in the stability and safety of the vehicle.

Key words: vehicle mass estimation, recursive least square(RLS) method, Kalman filtering, predictive cruise control(PCC）

摘要： 为精确估计重型卡车在预见性巡航控制过程中的质量，以车辆纵向动力学模型为基础，提出了车辆质量估计算法。利用卡尔曼滤波算法对发动机输出轴扭矩进行估计，并将其作为车辆质量估计算法的输入，基于递推最小二乘法对车辆质量进行了估计；对利用MATLAB/Simulink搭建的质量估计模型进行了C代码生成，并嵌入开发板中；最后对所提车辆质量估计算法进行了空载、1/3负载、满载实车道路试验。试验结果表明：所提算法在满载时的最大误差为8.87%，在1/3负载时的最大误差为7.43%，在空载时的最大误差为4.40%，能够满足车辆在预见性巡航控制过程中10%范围内的质量估计误差要求, 对车辆行驶的稳定性与安全性具有重要作用。

关键词: 车辆质量估计, 递推最小二乘法, 卡尔曼滤波, 预见性巡航控制