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  • ÀúÀÚXudong Zhang, Dietmar Go hlich Àú
  • ÃâÆÇ»ç¾ÆÁø
  • ÃâÆÇÀÏ2020-07-10
  • µî·ÏÀÏ2020-12-21
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The effect of vehicle active safety systems is subject to the friction force arising
from the contact of tires and the road surface. Therefore, an adequate knowledge
of the tire-road friction coefficient is of great importance to achieve a good
performance of these control systems. This paper presents a tire-road friction
coefficient estimation method for an advanced vehicle configuration,
four-motorized-wheel electric vehicles, in which the longitudinal tire force is
easily obtained. A hierarchical structure is adopted for the proposed estimation
design. An upper estimator is developed based on unscented Kalman filter to
estimate vehicle state information, while a hybrid estimation method is applied as
the lower estimator to identify the tire-road friction coefficient using general
regression neural network (GRNN) and Bayes¡¯ theorem. GRNN aims at detecting
road friction coefficient under small excitations, which are the most common
situations in daily driving. GRNN is able to accurately create a mapping from
input parameters to the friction coefficient, avoiding storing an entire complex tire
model. As for large excitations, the estimation algorithm is based on Bayes¡¯
theorem and a simplified ¡°magic formula¡± tire model. The integrated estimation
method is established by the combination of the above-mentioned estimators.
Finally, the simulations based on a high-fidelity CarSim vehicle model are carried
out on different road surfaces and driving maneuvers to verify the effectiveness of
the proposed estimation method.

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Á¦ 1Æí : SIMULINK ±âº»Æí
1.1 SIMULINKÀÇ ½ÃÀÛ 1
ºí·ÏÀÇ ¿¬°á 5
ºí·Ï ÆĶó¹ÌÅÍÀÇ ¼³Á¤ 7
½Ã¹Ä·¹À̼ǠÆĶó¹ÌÅÍ (Configuration Parameters)ÀÇ ¼³Á¤ 8
½Ã¹Ä·¹À̼ÇÀÇ ¼öÇà 9
ºí·Ï ÆĶó¹ÌÅÍÀǠǥ½Ã 9
º¹¼ö µ¥ÀÌÅÍÀǠǥ½Ã 11
2.2 µ¿Àû ½Ã¹Ä·¹À̼Ǡ13
ÀÌÂ÷ ¹ÌºÐ¹æÁ¤½Ä 17
¼±Çü »óź¯¼ö ¸ðµ¨ 23
DC ¸ðÅÍÀÇ ½Ã¹Ä·¹À̼Ǡ24
ÇÔ¼ö ºí·ÏÀÇ »ç¿ë 29
Â÷ºÐ¹æÁ¤½Ä(difference equation)ÀÇ ¸ðµ¨¸µ 34
Subsystem(ºÎ½Ã½ºÅÛ)ÀÇ ±¸¼º 37

Á¦ 2Æí : ¿¬±¸³í¹®
A hierarchical estimator development for estimation of tire-road
friction coefficient

1. Introduction 41
2. Vehicle modeling 42
3. Hierarchical estimation algorithm design 46
4. Hybrid estimator design for tire-road friction coefficient 49
5. Simulation results 53
6. Conclusion 57
7. References 60

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