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| 基于双层结构的商用车质量辨识算法 | |
| 引用本文： | 贾天乐,王洪亮,彭湃,薛冻,王显会. 基于双层结构的商用车质量辨识算法[J]. 北京理工大学学报, 2018, 38(S1): 89-92 |
| 作者姓名： | 贾天乐 王洪亮 彭湃 薛冻 王显会 |
| 作者单位： | 南京理工大学 机械工程学院, 江苏, 南京 210094,南京理工大学 机械工程学院, 江苏, 南京 210094,南京理工大学 机械工程学院, 江苏, 南京 210094,南京理工大学 机械工程学院, 江苏, 南京 210094,南京理工大学 机械工程学院, 江苏, 南京 210094 |
| 基金项目： | 国家自然科学基金资助项目（51205209，51205204）；江苏省六大人才高峰资助项目（2016—JXQC—020）；国家留学基金委资助项目（201606845008）；中央高校基本科研业务费专项资金资助（309171B8811） |
| 摘 要： | 针对山区路面商用车整车质量辨识问题，设计了一种汽车质量辨识算法.基于车辆纵向动力学模型提出了基于双层结构的商用车质量辨识算法：上层为基于倾角传感器的路面坡度估计算法；下层为基于带时变遗忘因子的递归最小二乘法的整车质量辨识算法.使用TruckSim软件平台分析了汽车悬架对上层算法的影响，并进行了实车试验.试验结果表明，所提出的质量辨识算法能够有效地估计路面坡度和整车质量，估计准确，收敛速度快，修正后的整车质量均方根误差平均值从209.97 kg减小到117.43 kg. |
| 关 键 词： | 商用车 坡度估计 汽车质量辨识 悬架挠度 |
| 收稿时间： | 2018-06-12 |
Double Layer Architecture algorithm for Vehicle Mass Estimation |
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| JIA Tian-le,WANG Hong-liang,PENG Pai,XUE Dong and WANG Xian-hui. Double Layer Architecture algorithm for Vehicle Mass Estimation[J]. Journal of Beijing Institute of Technology(Natural Science Edition), 2018, 38(S1): 89-92 | |
| Authors: | JIA Tian-le WANG Hong-liang PENG Pai XUE Dong WANG Xian-hui |
| Affiliation: | School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China,School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China,School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China,School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China and School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China |
| Abstract: | To solve the problem of commercial vehicle mass estimation in mountain area, a mass estimation algorithm was proposed for vehicle. Firstly, a double layer structure algorithm was designed based on a vehicle longitudinal dynamics model for commercial vehicle mass estimation. The upper layer of the algorithm was arranged for road grade estimation based on Tilt sensor, while the lower level of the algorithm was for the vehicle mass estimation based on the recursive least squares algorithm with time varying forgetting factor. Then, the influence of vehicle suspension on the upper layer algorithm was analyzed with TruckSim software, and the vehicle experiment was carried out. The experimental results show that the proposed algorithm can estimate road slope and vehicle mass effectively. The algorithm can improve the estimation accurate and the convergence speed. The mean square error of modified vehicle mass can be reduced from 209.97 kg to 117.43 kg. |
| Keywords: | commercial vehicle slope estimation vehicle mass estimation suspension deflection |
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