Prediction of Road Surface Friction Coefficient Using Only Macro- and Microtexture Measurements

2005 ◽  
Vol 131 (4) ◽  
pp. 311-319 ◽  
Author(s):  
Murat Ergun ◽  
Sukriye Iyinam ◽  
A. Faik Iyinam
Keyword(s):  
Friction Coefficient ◽  
Surface Friction ◽  
Road Surface

656 The influence of road surface friction coefficient on the effect of Collision Mitigation Brake : Investigation by using vehicle collision simulation

2009 ◽  
Vol 2009 (0) ◽  
pp. _656-1_-_656-6_
Author(s):  
Hirofumi MINAMOTO ◽  
Tetsushi MIMURO ◽  
Toshiya YOSHITANI ◽  
Koji SATO ◽  
Takeshi WATANABE ◽  
...  
Keyword(s):  
Friction Coefficient ◽  
Surface Friction ◽  
Road Surface ◽  
Vehicle Collision ◽  
Collision Mitigation

Measurement of road surface friction coefficient with strains on a side surface of a tire

2018 ◽  
Vol 2018 (0) ◽  
pp. OS0304
Author(s):  
Tomohiko SASANO ◽  
Yuya NANASAWA ◽  
Hiroshi TACHIYA ◽  
Masahiro HIGUCHI ◽  
Taisei ISE
Keyword(s):  
Friction Coefficient ◽  
Side Surface ◽  
Surface Friction ◽  
Road Surface

scholarly journals The Influence of Road Surface Friction Coefficient on the Effect of Collision Mitigation Brake

2010 ◽  
Vol 4 (6) ◽  
pp. 884-898
Author(s):  
Hirofumi MINAMOTO ◽  
Tetsushi MIMURO ◽  
Toshiya YOSHITANI ◽  
Koji SATO ◽  
Takeshi WATANABE ◽  
...  
Keyword(s):  
Friction Coefficient ◽  
Surface Friction ◽  
Road Surface ◽  
Collision Mitigation

Measurement of road surface friction coefficient in real-time with measuring the tire deformation

2020 ◽  
Vol 2020 (0) ◽  
pp. J18108
Author(s):  
Masashi KAWAGUCHI ◽  
Hiroshi TACHIYA ◽  
Masahiro HIGUCHI ◽  
Yousuke SUZUKI ◽  
Taisei ISE
Keyword(s):  
Friction Coefficient ◽  
Real Time ◽  
Surface Friction ◽  
Road Surface

Study on the Mechanical Properties and Handle of PTT/Wool Blended Fabrics

2010 ◽  
Vol 150-151 ◽  
pp. 1810-1813
Author(s):  
Guang Biao Xu ◽  
Lian Ying Zhao ◽  
Fu Mei Wang
Keyword(s):  
Mechanical Properties ◽  
Friction Coefficient ◽  
Surface Friction ◽  
Maximum Load ◽  
Wool Fabrics

The low-stress properties of PTT/wool blended fabrics were investigated by comparing with PET/wool blended fabrics. The results show that, the elongation of PTT/wool fabrics at the maximum load is higher, especially in filling wise, which means PTT/wool fabrics have better elastic. The bending hysteresis, shearing rigidity and shearing hysteresis of PTT/wool fabrics are higher than those of PET/wool fabrics, determining the fabrics having good formability. PTT/wool fabrics are easier to be compressed, and has a little higher surface friction coefficient, giving the fabrics better feeling of softness and fullness. The hand values show that PTT/wool blended fabrics are softer, and have the good bulkiness but not as good as that of PET/wool fabrics.

Slip-Clutch Torque Estimation via Real-Time Adaptive Lookup Table

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Wenpeng Wei ◽  
Hussein Dourra ◽  
Guoming Zhu
Keyword(s):  
Friction Coefficient ◽  
Real Time ◽  
Mean Squared Error ◽  
Surface Friction ◽  
Lookup Table ◽  
Recursive Least Squares ◽  
Time Varying ◽  
Squared Error ◽  
Output Torque ◽  
Clutch Torque

Abstract Transfer case clutch is crucial in determining traction torque distribution between front and rear tires for four-wheel-drive (4WD) vehicles. Estimating time-varying clutch surface friction coefficient is critical for traction torque control since it is proportional to the clutch output torque. As a result, this paper proposes a real-time adaptive lookup table strategy to provide the time-varying clutch surface friction coefficient. Specifically, the clutch-parameter-dependent (such as clutch output torque and clutch touchpoint distance) friction coefficient is first estimated with available low-cost vehicle sensors (such as wheel speed and vehicle acceleration); and then a clutch-parameter-independent approach is developed for clutch friction coefficient through a one-dimensional lookup table. The table nodes are adaptively updated based on a fast recursive least-squares (RLS) algorithm. Furthermore, the effectiveness of adaptive lookup table is demonstrated by comparing the estimated clutch torque from adaptive lookup table with that estimated from vehicle dynamics, which achieves 14.8 Nm absolute mean squared error (AMSE) and 2.66% relative mean squared error (RMSE).

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