Development of algorithms for commercial vehicle mass and road grade estimation

被引:2
|
作者
Seungki Kim
Kyungsik Shin
Changhee Yoo
Kunsoo Huh
机构
[1] Hanyang University,Department of Automotive Engineering
[2] Sangsin Brake,Department of Design
来源
International Journal of Automotive Technology | 2017年 / 18卷
关键词
Road grade; Vehicle mass; Kalman filter; Recursive least square; Forgetting factor;
D O I
暂无
中图分类号
学科分类号
摘要
Estimation algorithms for road slope angle and vehicle mass are presented for commercial vehicles. It is well known that vehicle weight and road grade significantly affect the longitudinal motion of a commercial vehicle. However, it is very difficult to measure the weight and road slope angle in real time because of lack of sensor technology. In addition, the total weight of a commercial vehicles varies depending on the freight. In this study, the road grade and vehicle mass estimation algorithms are proposed using the RLS (Recursive Least Square) method and only the in-vehicle sensors. The proposed algorithms are verified in experiments using a commercial vehicle under various conditions.
引用
收藏
页码:1077 / 1083
页数:6
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