Abstract: The convergence speed and estimation accuracy of the pavement adhesion coefficient algorithm are reduced due to the uncertainty of road condition and vehicle state excitation.This paper presents a road adhesion coefficient estimation algorithm based on adaptive strong tracking Kalman filter under fuzzy operating conditions.The fuzzy inference method is used to evaluate the excitation degree of the current vehicle state and output the covariance adjustment factor.A strong tracking factor is introduced to correct the Kalman filter algorithm in real time.By adjusting the covariance of the road adhesion factor,the convergence speed of the estimation algorithm is improved,and the strong tracking factor ensures that the algorithm is robust to disturbances from the road surface uncertainty.The estimation effect of the proposed algorithm is validated by a hardware-in-loop test bench.The experimental results show that the proposed estimation method can quickly converge near the true value under large excitation conditions and reduce the amplitude of the fluctuation of the estimated value under small excitation conditions.Compared with strong tracking KF algorithm and KF algorithm,the proposed algorithm markedly improves the algorithm convergence speed and estimation accuracy.