Identification of a Nonlinear Wheel/Rail Adhesion Model for Heavy-Duty Locomotives

Abstract

The optimal wheel/rail adhesion of heavy-duty locomotives under traction must be determined given that suboptimal wheel/rail adhesion may result in low creep utilization, skidding, and idling. Here, we present an algorithm for the online identification of adhesion parameters. The algorithm is used for the online parameter estimation of the nonlinear wheel/rail adhesion model. The factors that influence the wheel/rail adhesion–slip ratio relationship are analyzed and described using Burckhardt’s nonlinear model. Then, an identification model is established to obtain the corresponding likelihood function within the framework of parameter identification based on maximum likelihood. Given the nonlinearity of the problem, a modified differential evolution algorithm is used for the parameter estimation of the identification model to obtain an algorithm for the online estimation of the nonlinear adhesion model. Finally, numerical simulation experiments are conducted under different conditions. Experimental results show that the proposed algorithm can address the nonlinearity of the model and the uncertainty of the rail surface environment.

Keywords

• Advanced modeling
• differential evolution
• heavy-duty locomotive
• method of maximum likelihood
• nonlinear identification