A realtime online estimation algorithm on the adhesion performance parameters of the rail surface is presented for the problems such as low adhesion utilization, easy idling and easy slipping of the locomotive in the operation of heavy duty locomotive. Firstly, based on the analysis of locomotive adhesion behavior, Kiencke adhesioncreep model is selected as the identification model. Then, the algorithm uses model parameter identification framework under the significance of the maximum likelihood to transform the parameter estimation into solving quadratic programming problem, and the iterative algorithm for identification is constructed. At the same time, considering that the rail environment mutation cannot be measured, the timevarying forgetting factor is introduced into the identification algorithm to adapt to the switching of rail surface environment. The simulation results show that the algorithm is able to track the change of wheel rail environment timely, and identify the parameters of adhesion performance effectively.