基于云端地图的智能网联商用车质量估计算法研究

doi:10.19562/j.chinasae.qcgc.2024.06.007

Abstract

Abstract:

Vehicle mass is a key state variable of vehicle dynamics parameters. In the driver assistance system， accurate estimation of the vehicle mass is important for the planning and control algorithms. Traditional mass estimation algorithms face challenges in estimating road slope and vehicle mass at the same time. In particular， the error of slope estimation may seriously affect the accuracy of mass estimation. Currently， the cloud control platform provides high-precision road map information， which provides a new idea for further optimizing the mass estimation algorithm. Based on the vehicle-cloud collaborative framework of the cloud control platform， the system architecture of commercial vehicle mass estimation under the cloud control system is designed in this paper. Then， based on the extended Kalman filter theory， combining with the road map information in the cloud， the commercial vehicle mass estimation algorithm is developed. The vehicle mass is estimated by taking the road slope as a known parameter rather than a variable state parameter， and the algorithm is compared and verified by the driving data collected by the real vehicle test. The experimental results show that the mass estimation algorithm based on cloud slope information can achieve fast convergence under no-load and full-load conditions， and the absolute percentage error of the estimated mass is within 3%. Compared with the traditional algorithm of simultaneous estimation of mass and slope， it can converge to the real mass of the vehicle faster and more accurately.

Key words: intelligent and connected vehicle, cloud control system, mass estimation, extended Kalman filtering

Ao Zhang,Shuyan Li,Bolin Gao,Keke Wan,Guang Zhou,Tongyi Cao. Research on Mass Estimation Algorithm of Intelligent and Connected Commercial Vehicle Based on Cloud Road Map[J].Automotive Engineering, 2024, 46(6): 1006-1014.

Figures/Tables 13

参数	数值
车辆质量m/kg	49 000（满载）
车辆质量m/kg	16 800（空载）
主减速器传动比 $i g$	3.7
变速器传动比 $i o$	0.78（最高挡）
轮胎半径 $r w$ /m	0.502
迎风面积A/m²	9.78
风阻系数 $C D$	0.67
重力加速度g/（m·s^-2）	9.81
传动系机械效率 $η$	0.811 9
滚动阻力系数f	0.006

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质量估计算法类型	估计质量APE/%	估计质量RMSE/kg
不使用云端道路坡度信息	5.21	9 742
使用云端道路坡度信息	2.07	2 580

质量估计算法类型	估计质量APE/%	估计质量RMSE/kg
不使用云端道路坡度信息	5.80	6 184
使用云端道路坡度信息	2.78	4 671

设置质量/kg	油耗/kg	油耗增加率/%
49 000（真实质量）	2.255
46 550（95%质量）	2.263	0.35
51 450（105%质量）	2.274	0.84
44 100（90%质量）	2.270	0.66
53 900（110%质量）	2.266	0.48
39 200（80%质量）	2.288	1.46
58 800（120%质量）	2.284	1.28
32 900（半载质量）	2.551	13.12
16 800（空载质量）	2.606	15.56

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Research on Mass Estimation Algorithm of Intelligent and Connected Commercial Vehicle Based on Cloud Road Map

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