Estimation Strategy for the Adhesion Coefficient of Arbitrary Pavements Based on an Optimal Adaptive Fusion Algorithm

Accurately and quickly estimating the peak pavement adhesion coefficient is crucial for achieving high-quality driving and for optimizing vehicle stability control strategies. However, it also helps with putting forward higher requirements for vehicle driving states, tire model construction, the speed of the convergence, and the precision of the estimation algorithm. This paper unequivocally presents two highly effective methods for accurately estimating the peak pavement adhesion coefficient. Firstly, a new dimensionless tire model is constructed. A relationship between the mechanical tire characteristics and peak adhesion coefficient is established by using the Burckhardt model’s analogy between the adhesion coefficient and peak adhesion coefficient, and the UKE algorithm completes the estimation. Secondly, an adaptive variable universe fuzzy algorithm (AVUFS) is established using the follow-up of the adhesion coefficient between the tire and the road surface. Even if the slip rate is less than 5%, the algorithm can still complete accurate estimations and does not depend on the initial given information. Finally, using the estimation advantages of the two algorithms, fusion optimization is performed, and the best estimation result is obtained. Based on the simulation results, the algorithm can quickly and precisely predict the maximum pavement adhesion coefficient in situations where the pavement has a low or high adhesion coefficient.