Power Systems
M. Moradi; R. Havangi
Abstract
The rail vehicle dynamics are greatly impacted by the different forces present at the contact region of wheel and rail. Since the wheel and rail adhesion force is the key element in maintaining high braking and acceleration performance of rail vehicles, continuous monitoring of this factor ...
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The rail vehicle dynamics are greatly impacted by the different forces present at the contact region of wheel and rail. Since the wheel and rail adhesion force is the key element in maintaining high braking and acceleration performance of rail vehicles, continuous monitoring of this factor is very important. Damage to the infrastructures and lack of transportation convenience are the most obvious consequences of improper levels of adhesion force. Adhesion modeling is a time consuming task. In addition, due to the difficulties exist in directly measuring the adhesion force, the use of alternative methods in which state observers are adopted has attracted much attention. The selection of the primary model of the studied system and the ability of the selected estimator have a significant effect on the success of the proposed approach in estimating the variables. In this paper, the dynamics of the wheelset is simulated in the presence of irregularities that can be encountered in the railroad. Estimation of wheel-rail adhesion force is done indirectly by nonlinear filters as estimators and their accuracies in the estimation are compared to identify the better one. Meanwhile, inertial sensors (accelerometer and gyroscope) outputs are used as measuring matrix and employed to simulate actual situation and evaluate the estimators performances. To check the accuracy and ability of the estimators in estimating states and variables, the proposed approache implemented in Matlab.This study introduces an advantageous approach that uses the longitudinal, lateral and torsional dynamics to estimate wheel-rail adhesion force in variable conditions. Experimental results showed high precision, fast convergence, and low error values in variable estimation. Real time knowledge of the contact condition results in proper traction and braking performances. The results of proposed method can lead to decreasing wheel deterioration and operational costs, minimizing high creep levels, maximizing the use of already-existing adhesion, and mproving the frequency of service. It is worth noting that the proposed method is beneficial for both conventional railway transport and automated driverless trains.
